1 📦 Required Packages

This analysis requires several R packages for different aspects of RNA-seq data analysis:

Core Analysis:
- DESeq2: Differential expression analysis
- tidyverse: Data manipulation and visualization
- ggplot2: Advanced plotting
Visualization:
- EnhancedVolcano: Enhanced volcano plots
- pheatmap: Pretty heatmaps
- RColorBrewer: Color palettes
- Rtsne: t-SNE dimensionality reduction
Functional Analysis:
- clusterProfiler: Gene set enrichment analysis
- DOSE: Disease ontology
- enrichplot: Enrichment visualization
- org.Hs.eg.db: Human genome annotations
- pathview: Pathway visualization

# Core analysis packages
suppressPackageStartupMessages({
  library(DESeq2)
  library(ggplot2)
  library(tidyverse)
  
  # Visualization packages
  library(EnhancedVolcano)
  library(ggupset)
  library(pheatmap)
  library(RColorBrewer)
  library(Rtsne)
  
  # Functional analysis packages
  library(clusterProfiler)
  library(DOSE)
  library(enrichplot)
  library(org.Hs.eg.db)
  library(pathview)
  
  # Additional utilities
  library(biomaRt)
  library(cowplot)
  library(data.table)
  library(ggrepel)
  library(here)
  library(janitor)
  library(kableExtra)
  library(knitr)
  library(Matrix)
  library(pdftools)
  library(readr)
  library(scales)
  library(showtext)
  library(tibble)
  library(viridis)
})

2 📊 Analysis Overview

This RMarkdown document contains a comprehensive analysis of RNA-sequencing data comparing hypoxic and normoxic conditions in LNCaP and PC3 prostate cancer cell lines(Lu 2020). The analysis includes:

🔍 Quality control and data preprocessing
📈 Differential expression analysis
📊 Visualization of results
🛣️ Pathway and functional enrichment analysis
🔄 Comparative analysis between cell lines

🎯 Analysis Goals:

** Primary Objectives**
- Identify hypoxia-responsive genes
- Compare cell line-specific responses
- Discover potential therapeutic targets
Technical Approach
- Robust statistical analysis
- Comprehensive visualization
- Pathway-level interpretation
Expected Outcomes
- Gene expression signatures
- Pathway enrichment profiles
- Cell line comparison insights

This comprehensive RNA-seq analysis pipeline is structured in three main stages(He, Lin, and Chen 2022):

2.1 ↗️ Upstream Analysis

Quality control of FASTQ files (pre/post-trimming)
Read trimming
Genome alignment
Gene expression quantification

2.2 ➡️ Midstream Analysis

Count normalization
Exploratory data analysis
- Box plots
- Principal Component Analysis (PCA)
- t-SNE visualization
- Sample distance heatmap
Differential expression analysis
- MA plots
- Volcano plots
- Gene expression heatmaps

2.3 ⬇️Downstream Analysis

Functional enrichment analysis
- KEGG pathway analysis
- Gene Ontology (GO) enrichment
- Pathway visualization

Figure 1: Complete workflow of RNA-seq data analysis pipeline

3 🔬 Upstream Data Processing

3.1 📋 Project Overview

3.1.1 🔄 Pipeline Description

This comprehensive pipeline demonstrates end-to-end RNA-seq analysis, taking you from raw sequencing data to biological insights. The analysis workflow includes:

Raw data retrieval from GEO/SRA databases
Read processing using nf-core/rnaseq pipeline
Differential expression analysis with DESeq2
Advanced visualization and enrichment analysis

Note: If you are working with your own RNA-seq experiment data obtained directly from a sequencing facility, you can skip to the section on [Mapping reads using nf-core/rnaseq].

3.1.2 📊 Dataset Description

We analyze RNA-seq data from a study published in Nature Communications:

📚 Reference Publication:
Guo et al. Nature Communications 2019

Focus: Hypoxia response in prostate cancer cell lines

3.1.2.1 🧪 Experimental Design

The study examines two prostate cancer cell lines under different oxygen conditions:

LNCaP cells
- Empty vector control in normoxia
- Empty vector control in hypoxia
PC3 cells
- siControl in normoxia
- siControl in hypoxia

3.1.3 🧬 Sample Information

Sample Name	GSM ID	SRX ID	SRR IDs
LNCaP_Empty_Normoxia_rep1	GSM3145509	SRX4096735	SRR7179504-7
LNCaP_Empty_Normoxia_rep2	GSM3145510	SRX4096736	SRR7179508-11
LNCaP_Empty_Hypoxia_rep1	GSM3145513	SRX4096739	SRR7179520-3
LNCaP_Empty_Hypoxia_rep2	GSM3145514	SRX4096740	SRR7179524-7
PC3_siCtrl_Normoxia_rep1	GSM3145517	SRX4096743	SRR7179536
PC3_siCtrl_Normoxia_rep2	GSM3145518	SRX4096744	SRR7179537
PC3_siCtrl_Hypoxia_rep1	GSM3145521	SRX4096747	SRR7179540
PC3_siCtrl_Hypoxia_rep2	GSM3145522	SRX4096748	SRR7179541

3.2 📥 Data Download and Preparation

3.2.1 🛠️ Setting Up SRA Toolkit

To download the sequencing data, we’ll use the NCBI’s SRA toolkit, a powerful suite of tools for accessing sequencing data from the Sequence Read Archive.

Installation Note: - On Linux systems: sudo apt install sra-toolkit - For other systems: Visit the SRA Toolkit documentation

3.2.2 📝 Sample List Preparation

Create a file named list.txt containing the SRA run accessions:

cat << EOF > list.txt
SRR7179504 SRR7179505 SRR7179506 SRR7179507 SRR7179508 SRR7179509 SRR7179510 SRR7179511 SRR7179520 
SRR7179521 SRR7179522 SRR7179523 SRR7179524 SRR7179525 SRR7179526 SRR7179527 SRR7179536 SRR7179537 
SRR7179540 SRR7179541 
EOF

3.2.3 💻 Computing Environment

🖥️ Human Genetics Computing Cluster (HGCC)

Architecture:
- 1 head/login node
- 14 compute nodes
- 1 data transfer node (DTN)
Capabilities:
- NGS analysis pipelines
- High-performance computing
- Parallel computing
- Web application serving
- Data storage

We will utilize the HGCC’s computational resources for downloading and processing the SRA files into FASTQ format.

3.2.4 📥 Download SRA Files

First, we’ll download the SRA files using the SRA toolkit:

# Load SRA tools module
spack load sratools

# Download each SRA file
for i in $(cat list.txt); do
    echo "Processing $i at $(date)"
    fastq-dump \
        --gzip \
        --skip-technical \
        --readids \
        --read-filter pass \
        --dumpbase \
        --split-3 \
        --clip \
        --outdir rawData/ \
        $i
done

3.2.5 🔄 FASTQ File Processing

The downloaded data requires some processing before analysis. We’ll handle the LNCaP and PC3 samples differently due to their sequencing structure.

3.2.5.1 🧪 LNCaP Sample Processing

LNCaP samples have multiple sequencing runs that need to be concatenated:

# Process LNCaP Normoxia samples
echo "Processing LNCaP Normoxia samples..."
cat SRR7179504_pass.fastq.gz SRR7179505_pass.fastq.gz \
    SRR7179506_pass.fastq.gz SRR7179507_pass.fastq.gz \
    > LNCAP_Normoxia_S1.fastq.gz

cat SRR7179508_pass.fastq.gz SRR7179509_pass.fastq.gz \
    SRR7179510_pass.fastq.gz SRR7179511_pass.fastq.gz \
    > LNCAP_Normoxia_S2.fastq.gz

# Process LNCaP Hypoxia samples
echo "Processing LNCaP Hypoxia samples..."
cat SRR7179520_pass.fastq.gz SRR7179521_pass.fastq.gz \
    SRR7179522_pass.fastq.gz SRR7179523_pass.fastq.gz \
    > LNCAP_Hypoxia_S1.fastq.gz

cat SRR7179524_pass.fastq.gz SRR7179525_pass.fastq.gz \
    SRR7179526_pass.fastq.gz SRR7179527_pass.fastq.gz \
    > LNCAP_Hypoxia_S2.fastq.gz

3.2.5.2 PC3 Sample Processing

PC3 samples were sequenced in single runs, so we’ll simply rename them:

# Rename PC3 files
echo "Processing PC3 samples..."
mv SRR7179536_pass.fastq.gz PC3_Normoxia_S1.fastq.gz
mv SRR7179537_pass.fastq.gz PC3_Normoxia_S2.fastq.gz
mv SRR7179540_pass.fastq.gz PC3_Hypoxia_S1.fastq.gz
mv SRR7179541_pass.fastq.gz PC3_Hypoxia_S2.fastq.gz

3.2.5.3 🧹 Cleanup

Remove intermediate files to free up space:

# Remove individual SRA run files
echo "Cleaning up intermediate files..."
rm SRR*

✓ Completion Check: After processing, you should have 8 FASTQ files in your directory: - 4 LNCaP files (2 Normoxia, 2 Hypoxia) - 4 PC3 files (2 Normoxia, 2 Hypoxia)

These files are now ready for alignment to the reference genome!

3.3 🧬 Read Mapping with nf-core/rnaseq

3.3.1 🔄 Pipeline Overview

The nf-core/rnaseq pipeline provides a robust and standardized approach for RNA sequencing analysis(Ewels et al. 2020). It encompasses all necessary steps from raw FASTQ files to gene expression quantification.

Figure 2: NF-core/RNAseq workflow

3.3.2 📋 Sample Sheet Preparation

The pipeline requires a carefully formatted sample sheet that specifies the input files and their characteristics.

Key Requirements: 1️⃣ The first 4 columns must follow a specific format 2️⃣ Auto-detection of single-end vs paired-end data 3️⃣ Flexible additional columns for metadata

3.3.2.1 Sample Sheet Format

The sample sheet should be a CSV file with the following columns:sample,fastq_1,fastq_2,strandedness.

# Read your data
my_data <- read_csv("rawData/sample_sheet.csv")

Sample Sheet for nf-core/rnaseq Pipeline
sample	fastq_1	strandedness
LNCAP_Hypoxia_S1	rawData/LNCAP_Hypoxia_S1.fastq.gz	auto
LNCAP_Hypoxia_S2	rawData/LNCAP_Hypoxia_S2.fastq.gz	auto
LNCAP_Normoxia_S1	rawData/LNCAP_Normoxia_S1.fastq.gz	auto
LNCAP_Normoxia_S2	rawData/LNCAP_Normoxia_S2.fastq.gz	auto
PC3_Hypoxia_S1	rawData/PC3_Hypoxia_S1.fastq.gz	auto
PC3_Hypoxia_S2	rawData/PC3_Hypoxia_S2.fastq.gz	auto
PC3_Normoxia_S1	rawData/PC3_Normoxia_S1.fastq.gz	auto
PC3_Normoxia_S2	rawData/PC3_Normoxia_S2.fastq.gz	auto

3.3.3 🧬 Reference Genome Preparation

The pipeline requires two key reference files: 1. FASTA file (genome sequence) 2. GTF file (genome annotation)

3.3.3.1 📥 Download Reference Files

We’ll download the latest human reference genome from Ensembl:

# Get latest Ensembl release number
latest_release=$(curl -s 'http://rest.ensembl.org/info/software?content-type=application/json' | \
                grep -o '"release":[0-9]*' | cut -d: -f2)

# Download genome sequence
wget -L ftp://ftp.ensembl.org/pub/release-${latest_release}/fasta/homo_sapiens/dna/\
            Homo_sapiens.GRCh38.dna_sm.primary_assembly.fa.gz

# Download genome annotation
wget -L ftp://ftp.ensembl.org/pub/release-${latest_release}/gtf/homo_sapiens/\
            Homo_sapiens.GRCh38.${latest_release}.gtf.gz

💡 Tip: Always use the latest release of reference files for the most up-to-date genome annotations.

3.3.4 🚀 Pipeline Execution

Create a SLURM job script (run_nextflow.sh) to execute the pipeline:

#!/bin/bash

#SBATCH --job-name=Bulk_RNAseq            # Job name
#SBATCH --nodes=1                         # Run on a single node
#SBATCH --ntasks=32                       # Number of tasks (cores)
#SBATCH --mem=256gb                       # Memory requirement
#SBATCH --time=24:00:00                  # Time limit (24 hours)
#SBATCH --output=./SLURM_OUT/%x_%A.out   # Output file
#SBATCH --error=./SLURM_OUT/%x_%A.err    # Error file

# Print job info
echo "Starting job at: $(date)"
echo "Running on node: $(hostname)"
echo "Working directory: $(pwd)"
echo "Using ${SLURM_CPUS_ON_NODE} CPU cores"

# Run nf-core/rnaseq pipeline
nextflow run nf-core/rnaseq -r 3.16.0 \
    -profile apptainer \
    --input rawData/sample_sheet.csv \
    --outdir results \
    --fasta references/Homo_sapiens.GRCh38.dna.primary_assembly.fa.gz \
    --gtf references/Homo_sapiens.GRCh38.99.gtf.gz \
    --aligner star_salmon

Pipeline Parameters: - -r 3.16.0: Pipeline version - -profile apptainer: Container profile - --input: Sample sheet location - --outdir: Output directory - --fasta: Reference genome sequence - --gtf: Reference genome annotation - --aligner: STAR with Salmon quantification

3.3.4.1 🏃 Execute the Pipeline

Submit the job to SLURM:

# Submit the job
sbatch run_nextflow.sh

The output from successfully running the command looks like this:

executor >  local (288)
[4d/e88d76] NFC…na.primary_assembly.fa.gz) | 1 of 1 ✔
[f3/04cb10] NFC…_sapiens.GRCh38.99.gtf.gz) | 1 of 1 ✔
[97/0a1dac] NFC…8.dna.primary_assembly.fa) | 1 of 1 ✔
[ec/1f96a8] NFC…ary_assembly.filtered.gtf) | 1 of 1 ✔
[c8/4075f2] NFC…8.dna.primary_assembly.fa) | 1 of 1 ✔
[b1/2eebb8] NFC…8.dna.primary_assembly.fa) | 1 of 1 ✔
[6b/7aca1c] NFC…8.dna.primary_assembly.fa) | 1 of 1 ✔
[-        ] NFC…_SETSTRANDEDNESS:CAT_FASTQ -
[37/51a998] NFC…E:FASTQC (PC3_Normoxia_S2) | 8 of 8 ✔
[89/2db109] NFC…RIMGALORE (PC3_Hypoxia_S2) | 8 of 8 ✔
[2a/6f0f4a] NFC…EX (genome.transcripts.fa) | 1 of 1 ✔
[1b/69a515] NFC…SAMPLE (LNCAP_Normoxia_S2) | 8 of 8 ✔
[de/1db031] NFC…_QUANT (LNCAP_Normoxia_S1) | 8 of 8 ✔
[50/531a76] NFC…_ALIGN (LNCAP_Normoxia_S1) | 8 of 8 ✔
[46/33ae9a] NFC…S_SORT (LNCAP_Normoxia_S1) | 8 of 8 ✔
[c7/5e2724] NFC…_INDEX (LNCAP_Normoxia_S1) | 8 of 8 ✔
[4c/d2378a] NFC…_STATS (LNCAP_Normoxia_S1) | 8 of 8 ✔
[cc/75a2cd] NFC…FLAGSTAT (PC3_Normoxia_S1) | 8 of 8 ✔
[fa/09e140] NFC…DXSTATS (LNCAP_Hypoxia_S2) | 8 of 8 ✔
[60/948088] NFC…_QUANT (LNCAP_Normoxia_S1) | 8 of 8 ✔
[26/d9c087] NFC…LMON:CUSTOM_TX2GENE (null) | 1 of 1 ✔
[3d/2c8362] NFC…AR_SALMON:TXIMETA_TXIMPORT | 1 of 1 ✔
[0a/e83622] NFC…LMON:SE_GENE (all_samples) | 1 of 1 ✔
[cf/5dbd84] NFC…ENGTH_SCALED (all_samples) | 1 of 1 ✔
[64/67bd55] NFC…_GENE_SCALED (all_samples) | 1 of 1 ✔
[18/566451] NFC…E_TRANSCRIPT (all_samples) | 1 of 1 ✔
[fe/0cfd95] NFC…ASEQ:DESEQ2_QC_STAR_SALMON | 1 of 1 ✔
[28/ca3860] NFC…ICATES (LNCAP_Normoxia_S1) | 8 of 8 ✔
[60/c7e69b] NFC…_INDEX (LNCAP_Normoxia_S1) | 8 of 8 ✔
[82/03ce63] NFC…OLS_STATS (PC3_Hypoxia_S1) | 8 of 8 ✔
[ee/c48141] NFC…FLAGSTAT (PC3_Normoxia_S1) | 8 of 8 ✔
[18/d3254b] NFC…_IDXSTATS (PC3_Hypoxia_S1) | 8 of 8 ✔
[2b/777e77] NFC…INGTIE (LNCAP_Normoxia_S1) | 8 of 8 ✔
[35/91ff22] NFC…COUNTS (LNCAP_Normoxia_S1) | 8 of 8 ✔
[f7/4a9a08] NFC…IOTYPE (LNCAP_Normoxia_S1) | 8 of 8 ✔
[d8/563ed2] NFC…COV_FW (LNCAP_Normoxia_S1) | 8 of 8 ✔
[f1/0c38f3] NFC…OV_REV (LNCAP_Normoxia_S1) | 8 of 8 ✔
[f4/ca548d] NFC…EDCLIP (LNCAP_Normoxia_S1) | 8 of 8 ✔
[a6/a43b1a] NFC…BIGWIG (LNCAP_Normoxia_S1) | 8 of 8 ✔
[81/0c3b16] NFC…EDCLIP (LNCAP_Normoxia_S1) | 8 of 8 ✔
[cd/2fa162] NFC…BIGWIG (LNCAP_Normoxia_S1) | 8 of 8 ✔
[4a/005a31] NFC…RNASEQ (LNCAP_Normoxia_S1) | 8 of 8 ✔
[48/1ba811] NFC…PRADAR (LNCAP_Normoxia_S1) | 8 of 8 ✔
[db/e887db] NFC…AMSTAT (LNCAP_Normoxia_S1) | 8 of 8 ✔
[dc/943607] NFC…STANCE (LNCAP_Normoxia_S1) | 8 of 8 ✔
[81/a0ba9f] NFC…RIMENT (LNCAP_Normoxia_S1) | 8 of 8 ✔
[45/944d35] NFC…TATION (LNCAP_Normoxia_S1) | 8 of 8 ✔
[c6/32ba39] NFC…RATION (LNCAP_Normoxia_S1) | 8 of 8 ✔
[69/38729b] NFC…BUTION (LNCAP_Normoxia_S1) | 8 of 8 ✔
[6d/91dec1] NFC…CATION (LNCAP_Normoxia_S1) | 8 of 8 ✔
[6c/b8daaf] NFC…_RNASEQ:RNASEQ:MULTIQC (1) | 1 of 1 ✔
-[nf-core/rnaseq] Pipeline completed successfully -
Completed at: 30-May-2025 22:26:13
Duration    : 2h 50m 30s
CPU hours   : 63.9
Succeeded   : 288

After the script has finished running, the output folders containing the alignment files for each sample should be found in the directory star_salmon/.

3.3.5 📊 Understanding the output

🗂️ In the star_salmon/ directory, we will find a separate folder of results for each of the FASTQ files you mapped. Inside this folder, you should see the following files:

.
├── bigwig/
│   ├── LNCAP_Hypoxia_S1.forward.bigWig
│   ├── LNCAP_Hypoxia_S1.reverse.bigWig
│   ├── LNCAP_Hypoxia_S2.forward.bigWig
│   ├── LNCAP_Hypoxia_S2.reverse.bigWig
│   ├── LNCAP_Normoxia_S1.forward.bigWig
│   ├── LNCAP_Normoxia_S1.reverse.bigWig
│   ├── LNCAP_Normoxia_S2.forward.bigWig
│   ├── LNCAP_Normoxia_S2.reverse.bigWig
│   ├── PC3_Hypoxia_S1.forward.bigWig
│   ├── PC3_Hypoxia_S1.reverse.bigWig
│   ├── PC3_Hypoxia_S2.forward.bigWig
│   ├── PC3_Hypoxia_S2.reverse.bigWig
│   ├── PC3_Normoxia_S1.forward.bigWig
│   ├── PC3_Normoxia_S1.reverse.bigWig
│   ├── PC3_Normoxia_S2.forward.bigWig
│   └── PC3_Normoxia_S2.reverse.bigWig
├── deseq2_qc/
│   ├── size_factors/
│   ├── deseq2.dds.RData
│   ├── deseq2.pca.vals.txt
│   ├── deseq2.plots.pdf
│   ├── deseq2.sample.dists.txt
│   └── R_sessionInfo.log
├── dupradar/
│   ├── box_plot/
│   ├── gene_data/
│   ├── histogram/
│   ├── intercepts_slope/
│   └── scatter_plot/
├── featurecounts/
│   ├── LNCAP_Hypoxia_S1.biotype_counts_mqc.tsv
│   ├── LNCAP_Hypoxia_S1.biotype_counts_rrna_mqc.tsv
│   ├── LNCAP_Hypoxia_S1.featureCounts.txt
│   ├── LNCAP_Hypoxia_S1.featureCounts.txt.summary
│   ├── LNCAP_Hypoxia_S2.biotype_counts_mqc.tsv
│   ├── LNCAP_Hypoxia_S2.biotype_counts_rrna_mqc.tsv
│   ├── LNCAP_Hypoxia_S2.featureCounts.txt
│   ├── LNCAP_Hypoxia_S2.featureCounts.txt.summary
│   ├── LNCAP_Normoxia_S1.biotype_counts_mqc.tsv
│   ├── LNCAP_Normoxia_S1.biotype_counts_rrna_mqc.tsv
│   ├── LNCAP_Normoxia_S1.featureCounts.txt
│   ├── LNCAP_Normoxia_S1.featureCounts.txt.summary
│   ├── LNCAP_Normoxia_S2.biotype_counts_mqc.tsv
│   ├── LNCAP_Normoxia_S2.biotype_counts_rrna_mqc.tsv
│   ├── LNCAP_Normoxia_S2.featureCounts.txt
│   ├── LNCAP_Normoxia_S2.featureCounts.txt.summary
│   ├── PC3_Hypoxia_S1.biotype_counts_mqc.tsv
│   ├── PC3_Hypoxia_S1.biotype_counts_rrna_mqc.tsv
│   ├── PC3_Hypoxia_S1.featureCounts.txt
│   ├── PC3_Hypoxia_S1.featureCounts.txt.summary
│   ├── PC3_Hypoxia_S2.biotype_counts_mqc.tsv
│   ├── PC3_Hypoxia_S2.biotype_counts_rrna_mqc.tsv
│   ├── PC3_Hypoxia_S2.featureCounts.txt
│   ├── PC3_Hypoxia_S2.featureCounts.txt.summary
│   ├── PC3_Normoxia_S1.biotype_counts_mqc.tsv
│   ├── PC3_Normoxia_S1.biotype_counts_rrna_mqc.tsv
│   ├── PC3_Normoxia_S1.featureCounts.txt
│   ├── PC3_Normoxia_S1.featureCounts.txt.summary
│   ├── PC3_Normoxia_S2.biotype_counts_mqc.tsv
│   ├── PC3_Normoxia_S2.biotype_counts_rrna_mqc.tsv
│   ├── PC3_Normoxia_S2.featureCounts.txt
│   └── PC3_Normoxia_S2.featureCounts.txt.summary
├── LNCAP_Hypoxia_S1/
│   ├── aux_info/
│   ├── libParams/
│   ├── logs/
│   ├── cmd_info.json
│   ├── quant.genes.sf
│   └── quant.sf
├── LNCAP_Hypoxia_S2/
│   ├── aux_info/
│   ├── libParams/
│   ├── logs/
│   ├── cmd_info.json
│   ├── quant.genes.sf
│   └── quant.sf
├── LNCAP_Normoxia_S1/
│   ├── aux_info/
│   ├── libParams/
│   ├── logs/
│   ├── cmd_info.json
│   ├── quant.genes.sf
│   └── quant.sf
├── LNCAP_Normoxia_S2/
│   ├── aux_info/
│   ├── libParams/
│   ├── logs/
│   ├── cmd_info.json
│   ├── quant.genes.sf
│   └── quant.sf
├── log/
│   ├── LNCAP_Hypoxia_S1.Log.final.out
│   ├── LNCAP_Hypoxia_S1.Log.out
│   ├── LNCAP_Hypoxia_S1.Log.progress.out
│   ├── LNCAP_Hypoxia_S1.SJ.out.tab
│   ├── LNCAP_Hypoxia_S2.Log.final.out
│   ├── LNCAP_Hypoxia_S2.Log.out
│   ├── LNCAP_Hypoxia_S2.Log.progress.out
│   ├── LNCAP_Hypoxia_S2.SJ.out.tab
│   ├── LNCAP_Normoxia_S1.Log.final.out
│   ├── LNCAP_Normoxia_S1.Log.out
│   ├── LNCAP_Normoxia_S1.Log.progress.out
│   ├── LNCAP_Normoxia_S1.SJ.out.tab
│   ├── LNCAP_Normoxia_S2.Log.final.out
│   ├── LNCAP_Normoxia_S2.Log.out
│   ├── LNCAP_Normoxia_S2.Log.progress.out
│   ├── LNCAP_Normoxia_S2.SJ.out.tab
│   ├── PC3_Hypoxia_S1.Log.final.out
│   ├── PC3_Hypoxia_S1.Log.out
│   ├── PC3_Hypoxia_S1.Log.progress.out
│   ├── PC3_Hypoxia_S1.SJ.out.tab
│   ├── PC3_Hypoxia_S2.Log.final.out
│   ├── PC3_Hypoxia_S2.Log.out
│   ├── PC3_Hypoxia_S2.Log.progress.out
│   ├── PC3_Hypoxia_S2.SJ.out.tab
│   ├── PC3_Normoxia_S1.Log.final.out
│   ├── PC3_Normoxia_S1.Log.out
│   ├── PC3_Normoxia_S1.Log.progress.out
│   ├── PC3_Normoxia_S1.SJ.out.tab
│   ├── PC3_Normoxia_S2.Log.final.out
│   ├── PC3_Normoxia_S2.Log.out
│   ├── PC3_Normoxia_S2.Log.progress.out
│   └── PC3_Normoxia_S2.SJ.out.tab
├── PC3_Hypoxia_S1/
│   ├── aux_info/
│   ├── libParams/
│   ├── logs/
│   ├── cmd_info.json
│   ├── quant.genes.sf
│   └── quant.sf
├── PC3_Hypoxia_S2/
│   ├── aux_info/
│   ├── libParams/
│   ├── logs/
│   ├── cmd_info.json
│   ├── quant.genes.sf
│   └── quant.sf
├── PC3_Normoxia_S1/
│   ├── aux_info/
│   ├── libParams/
│   ├── logs/
│   ├── cmd_info.json
│   ├── quant.genes.sf
│   └── quant.sf
├── PC3_Normoxia_S2/
│   ├── aux_info/
│   ├── libParams/
│   ├── logs/
│   ├── cmd_info.json
│   ├── quant.genes.sf
│   └── quant.sf
├── picard_metrics/
│   ├── LNCAP_Hypoxia_S1.markdup.sorted.MarkDuplicates.metrics.txt
│   ├── LNCAP_Hypoxia_S2.markdup.sorted.MarkDuplicates.metrics.txt
│   ├── LNCAP_Normoxia_S1.markdup.sorted.MarkDuplicates.metrics.txt
│   ├── LNCAP_Normoxia_S2.markdup.sorted.MarkDuplicates.metrics.txt
│   ├── PC3_Hypoxia_S1.markdup.sorted.MarkDuplicates.metrics.txt
│   ├── PC3_Hypoxia_S2.markdup.sorted.MarkDuplicates.metrics.txt
│   ├── PC3_Normoxia_S1.markdup.sorted.MarkDuplicates.metrics.txt
│   └── PC3_Normoxia_S2.markdup.sorted.MarkDuplicates.metrics.txt
├── qualimap/
│   ├── LNCAP_Hypoxia_S1/
│   ├── LNCAP_Hypoxia_S2/
│   ├── LNCAP_Normoxia_S1/
│   ├── LNCAP_Normoxia_S2/
│   ├── PC3_Hypoxia_S1/
│   ├── PC3_Hypoxia_S2/
│   ├── PC3_Normoxia_S1/
│   └── PC3_Normoxia_S2/
├── rseqc/
│   ├── bam_stat/
│   ├── infer_experiment/
│   ├── inner_distance/
│   ├── junction_annotation/
│   ├── junction_saturation/
│   ├── read_distribution/
│   └── read_duplication/
├── samtools_stats/
│   ├── LNCAP_Hypoxia_S1.markdup.sorted.bam.flagstat
│   ├── LNCAP_Hypoxia_S1.markdup.sorted.bam.idxstats
│   ├── LNCAP_Hypoxia_S1.markdup.sorted.bam.stats
│   ├── LNCAP_Hypoxia_S1.sorted.bam.flagstat
│   ├── LNCAP_Hypoxia_S1.sorted.bam.idxstats
│   ├── LNCAP_Hypoxia_S1.sorted.bam.stats
│   ├── LNCAP_Hypoxia_S2.markdup.sorted.bam.flagstat
│   ├── LNCAP_Hypoxia_S2.markdup.sorted.bam.idxstats
│   ├── LNCAP_Hypoxia_S2.markdup.sorted.bam.stats
│   ├── LNCAP_Hypoxia_S2.sorted.bam.flagstat
│   ├── LNCAP_Hypoxia_S2.sorted.bam.idxstats
│   ├── LNCAP_Hypoxia_S2.sorted.bam.stats
│   ├── LNCAP_Normoxia_S1.markdup.sorted.bam.flagstat
│   ├── LNCAP_Normoxia_S1.markdup.sorted.bam.idxstats
│   ├── LNCAP_Normoxia_S1.markdup.sorted.bam.stats
│   ├── LNCAP_Normoxia_S1.sorted.bam.flagstat
│   ├── LNCAP_Normoxia_S1.sorted.bam.idxstats
│   ├── LNCAP_Normoxia_S1.sorted.bam.stats
│   ├── LNCAP_Normoxia_S2.markdup.sorted.bam.flagstat
│   ├── LNCAP_Normoxia_S2.markdup.sorted.bam.idxstats
│   ├── LNCAP_Normoxia_S2.markdup.sorted.bam.stats
│   ├── LNCAP_Normoxia_S2.sorted.bam.flagstat
│   ├── LNCAP_Normoxia_S2.sorted.bam.idxstats
│   ├── LNCAP_Normoxia_S2.sorted.bam.stats
│   ├── PC3_Hypoxia_S1.markdup.sorted.bam.flagstat
│   ├── PC3_Hypoxia_S1.markdup.sorted.bam.idxstats
│   ├── PC3_Hypoxia_S1.markdup.sorted.bam.stats
│   ├── PC3_Hypoxia_S1.sorted.bam.flagstat
│   ├── PC3_Hypoxia_S1.sorted.bam.idxstats
│   ├── PC3_Hypoxia_S1.sorted.bam.stats
│   ├── PC3_Hypoxia_S2.markdup.sorted.bam.flagstat
│   ├── PC3_Hypoxia_S2.markdup.sorted.bam.idxstats
│   ├── PC3_Hypoxia_S2.markdup.sorted.bam.stats
│   ├── PC3_Hypoxia_S2.sorted.bam.flagstat
│   ├── PC3_Hypoxia_S2.sorted.bam.idxstats
│   ├── PC3_Hypoxia_S2.sorted.bam.stats
│   ├── PC3_Normoxia_S1.markdup.sorted.bam.flagstat
│   ├── PC3_Normoxia_S1.markdup.sorted.bam.idxstats
│   ├── PC3_Normoxia_S1.markdup.sorted.bam.stats
│   ├── PC3_Normoxia_S1.sorted.bam.flagstat
│   ├── PC3_Normoxia_S1.sorted.bam.idxstats
│   ├── PC3_Normoxia_S1.sorted.bam.stats
│   ├── PC3_Normoxia_S2.markdup.sorted.bam.flagstat
│   ├── PC3_Normoxia_S2.markdup.sorted.bam.idxstats
│   ├── PC3_Normoxia_S2.markdup.sorted.bam.stats
│   ├── PC3_Normoxia_S2.sorted.bam.flagstat
│   ├── PC3_Normoxia_S2.sorted.bam.idxstats
│   └── PC3_Normoxia_S2.sorted.bam.stats
├── stringtie/
│   ├── LNCAP_Hypoxia_S1.ballgown/
│   ├── LNCAP_Hypoxia_S2.ballgown/
│   ├── LNCAP_Normoxia_S1.ballgown/
│   ├── LNCAP_Normoxia_S2.ballgown/
│   ├── PC3_Hypoxia_S1.ballgown/
│   ├── PC3_Hypoxia_S2.ballgown/
│   ├── PC3_Normoxia_S1.ballgown/
│   ├── PC3_Normoxia_S2.ballgown/
│   ├── LNCAP_Hypoxia_S1.coverage.gtf
│   ├── LNCAP_Hypoxia_S1.gene.abundance.txt
│   ├── LNCAP_Hypoxia_S1.transcripts.gtf
│   ├── LNCAP_Hypoxia_S2.coverage.gtf
│   ├── LNCAP_Hypoxia_S2.gene.abundance.txt
│   ├── LNCAP_Hypoxia_S2.transcripts.gtf
│   ├── LNCAP_Normoxia_S1.coverage.gtf
│   ├── LNCAP_Normoxia_S1.gene.abundance.txt
│   ├── LNCAP_Normoxia_S1.transcripts.gtf
│   ├── LNCAP_Normoxia_S2.coverage.gtf
│   ├── LNCAP_Normoxia_S2.gene.abundance.txt
│   ├── LNCAP_Normoxia_S2.transcripts.gtf
│   ├── PC3_Hypoxia_S1.coverage.gtf
│   ├── PC3_Hypoxia_S1.gene.abundance.txt
│   ├── PC3_Hypoxia_S1.transcripts.gtf
│   ├── PC3_Hypoxia_S2.coverage.gtf
│   ├── PC3_Hypoxia_S2.gene.abundance.txt
│   ├── PC3_Hypoxia_S2.transcripts.gtf
│   ├── PC3_Normoxia_S1.coverage.gtf
│   ├── PC3_Normoxia_S1.gene.abundance.txt
│   ├── PC3_Normoxia_S1.transcripts.gtf
│   ├── PC3_Normoxia_S2.coverage.gtf
│   ├── PC3_Normoxia_S2.gene.abundance.txt
│   └── PC3_Normoxia_S2.transcripts.gtf
├── LNCAP_Hypoxia_S1.markdup.sorted.bam
├── LNCAP_Hypoxia_S1.markdup.sorted.bam.bai
├── LNCAP_Hypoxia_S2.markdup.sorted.bam
├── LNCAP_Hypoxia_S2.markdup.sorted.bam.bai
├── LNCAP_Normoxia_S1.markdup.sorted.bam
├── LNCAP_Normoxia_S1.markdup.sorted.bam.bai
├── LNCAP_Normoxia_S2.markdup.sorted.bam
├── LNCAP_Normoxia_S2.markdup.sorted.bam.bai
├── null.merged.gene_counts_length_scaled.SummarizedExperiment.rds
├── null.merged.gene_counts_scaled.SummarizedExperiment.rds
├── null.merged.gene_counts.SummarizedExperiment.rds
├── null.merged.transcript_counts.SummarizedExperiment.rds
├── PC3_Hypoxia_S1.markdup.sorted.bam
├── PC3_Hypoxia_S1.markdup.sorted.bam.bai
├── PC3_Hypoxia_S2.markdup.sorted.bam
├── PC3_Hypoxia_S2.markdup.sorted.bam.bai
├── PC3_Normoxia_S1.markdup.sorted.bam
├── PC3_Normoxia_S1.markdup.sorted.bam.bai
├── PC3_Normoxia_S2.markdup.sorted.bam
├── PC3_Normoxia_S2.markdup.sorted.bam.bai
├── salmon.merged.gene_counts_length_scaled.tsv
├── salmon.merged.gene_counts_scaled.tsv
├── salmon.merged.gene_counts.tsv
├── salmon.merged.gene_lengths.tsv
├── salmon.merged.gene_tpm.tsv
├── salmon.merged.transcript_counts.tsv
├── salmon.merged.transcript_lengths.tsv
├── salmon.merged.transcript_tpm.tsv
└── tx2gene.tsv

4 🎡 Differential Expression Analysis

4.1 📊 Introduction to DESeq2

DESeq2 is a robust method for differential expression analysis(Love, Huber, and Anders 2014) that:

Uses negative binomial distribution to model count data
Estimates size factors for library normalization
Calculates dispersion estimates
Performs statistical tests for differential expression

🎯 Analysis Objectives: 1. 🔍 Identify genes differentially expressed between: - 🌡️ Hypoxic vs normoxic conditions - 🧫 LNCaP and PC3 cell lines 2. 🔄 Account for batch effects and experimental variables 3. 📊 Generate visualizations to validate results

4.2 📥 Data Import and Preprocessing

Let’s start by examining our count data from the RNA-seq pipeline:

# Read and process count data
data <- read.table(
  "data/salmon.merged.gene_counts_length_scaled.tsv", 
  header = TRUE, 
  row.names = 1
)

# Sort columns and round to integers
data <- data[, sort(colnames(data))]
data_round <- round(data[2:9], digits = 0)

# Preview the data
head(data_round)

Data Structure: - 60,676 rows (genes) - 8 columns (samples) - Values represent raw read counts per gene per sample

4.2.1 📊 Examining Sequencing Depth

Let’s check the total number of reads (sequencing depth) for each sample:

# Calculate total reads per sample
total_reads <- colSums(data_round[1:8])

# Create a data frame for display
reads_df <- data.frame(
  Sample = names(total_reads),
  Total_Reads = total_reads,
  Total_Reads_Millions = round(total_reads / 1e6, 2),
  row.names = NULL
)
reads_df

Sequencing Depth Analysis:

Sample depth varies considerably: - Highest: LNCAP_Normoxia_S2 (~57M reads) - Lowest: PC3_Normoxia_S1 (~17M reads)

Key Observations: 1. Over 3-fold difference between highest and lowest depth 2. PC3_Hypoxia_S2 has notably low depth (~17M reads) 3. Most samples have 40-55M reads

Implications: - Direct comparison of raw counts not possible - Normalization required to account for depth differences - DESeq2 will handle this through size factor estimation

4.2.2 ⚖️ Need for Normalization

Before proceeding with differential expression analysis, we need to account for:

Library Size Differences
- Different total read counts between samples
- Affects absolute expression values
Technical Biases
- Sequencing depth variations
- RNA composition differences
- GC content bias

💡 Note: DESeq2 handles normalization through: - Size factor estimation - Variance stabilizing transformation - Dispersion estimation

4.3 ⚙️ DESeq2 Setup and Configuration

4.3.1 🔧 Creating the DESeq2 Object

A DESeq2 analysis requires three key components:

Required Components:

countData
- Integer matrix of gene counts
- Rows = genes, Columns = samples
- Generated by Salmon quantification
colData
- Sample metadata/experimental design
- Treatment conditions
- Batch information
- Other covariates
design Formula
- Specifies model for testing
- Variables to compare
- Additional factors to consider

4.3.2 📋 Preparing Sample Groups

First, let’s define our experimental conditions:

# Define experimental conditions
condition <- c(
  rep("LNCAP_Hypoxia", 2),  # LNCaP hypoxia replicates
  rep("LNCAP_Normoxia", 2), # LNCaP normoxia replicates
  rep("PC3_Hypoxia", 2),    # PC3 hypoxia replicates
  rep("PC3_Normoxia", 2)    # PC3 normoxia replicates
)

4.3.3 📝 Creating Sample Metadata

Next, create a data frame with sample information:

# Create sample metadata data frame
my_colData <- data.frame(
  rownames = colnames(data_round)[1:8],  # Sample names
  condition = factor(condition)           # Experimental conditions
)

# Reorder columns and display
my_colData <- my_colData[, c("rownames", "condition")]
my_colData

4.3.4 Initializing the DESeq2 Object

With our count data and sample metadata prepared, we can create the DESeq2 object:

# Create DESeqDataSet object
dds <- DESeqDataSetFromMatrix(
  countData = data_round,     # Count matrix
  colData = my_colData,       # Sample metadata
  design = ~ condition        # Experimental design
)

# Display object information
dds

#> class: DESeqDataSet 
#> dim: 60676 8 
#> metadata(1): version
#> assays(1): counts
#> rownames(60676): ENSG00000000003 ENSG00000000005 ... ENSG00000288587 ENSG00000288588
#> rowData names(0):
#> colnames(8): LNCAP_Hypoxia_S1 LNCAP_Hypoxia_S2 ... PC3_Normoxia_S1 PC3_Normoxia_S2
#> colData names(2): rownames condition

4.3.5 🚀 Running the DESeq2 Analysis

The DESeq() function performs the core differential expression analysis through several steps:

Analysis Steps:

Size Factor Estimation
- Normalizes for sequencing depth
- Accounts for library size differences
Dispersion Estimation
- Calculates gene-wise dispersion
- Fits mean-dispersion relationship
- Shrinks estimates toward trend line
Model Fitting and Testing
- Fits negative binomial GLM
- Performs Wald tests
- Calculates p-values

Let’s run the analysis:

# Run DESeq2 analysis
dds <- DESeq(dds)

# View results summary
dds

#> class: DESeqDataSet 
#> dim: 60676 8 
#> metadata(1): version
#> assays(4): counts mu H cooks
#> rownames(60676): ENSG00000000003 ENSG00000000005 ... ENSG00000288587 ENSG00000288588
#> rowData names(30): baseMean baseVar ... deviance maxCooks
#> colnames(8): LNCAP_Hypoxia_S1 LNCAP_Hypoxia_S2 ... PC3_Normoxia_S1 PC3_Normoxia_S2
#> colData names(3): rownames condition sizeFactor

4.3.6 🔍 Examining DESeq2 Results

The DESeq2 object contains various data components that we can inspect:

Available Data: - Raw counts - Normalized counts - Size factors - Dispersions - Model coefficients - Test statistics

4.3.6.1 📊 Raw Count Data

Let’s examine the raw count matrix:

# View raw counts for first few genes
dds@assays@data$counts

	LNCAP_Hypoxia_S1	LNCAP_Hypoxia_S2	LNCAP_Normoxia_S1	LNCAP_Normoxia_S2	PC3_Hypoxia_S1	PC3_Hypoxia_S2	PC3_Normoxia_S1	PC3_Normoxia_S2
ENSG00000000003	694	652	366	332	1087	384	336	865
ENSG00000000005	0	0	0	0	0	0	0	0
ENSG00000000419	4317	4761	4969	5939	3127	995	962	2374
ENSG00000000457	725	789	566	648	99	30	30	94
ENSG00000000460	351	393	419	407	454	206	258	601
ENSG00000000938	2	1	2	2	0	0	0	2

Key Observations: 1. Counts vary widely between genes 2. Some genes show consistent expression patterns 3. Many genes have zero counts in some samples 4. Expression patterns differ between cell lines

4.3.6.2 ⚖️ Normalized Count Data

DESeq2 provides size-factor normalized counts that account for sequencing depth differences:

# Get normalized counts
normalized_counts <- counts(dds, normalized = TRUE)

	LNCAP_Hypoxia_S1	LNCAP_Hypoxia_S2	LNCAP_Normoxia_S1	LNCAP_Normoxia_S2	PC3_Hypoxia_S1	PC3_Hypoxia_S2	PC3_Normoxia_S1	PC3_Normoxia_S2
ENSG00000000003	385.26	313.23	192.03	144.41	1382	1302	990.4	1017.99
ENSG00000000005	0.00	0.00	0.00	0.00	0	0	0.0	0.00
ENSG00000000419	2396.52	2287.27	2607.16	2583.34	3975	3373	2835.6	2793.88
ENSG00000000457	402.47	379.05	296.97	281.87	126	102	88.4	110.63
ENSG00000000460	194.85	188.81	219.84	177.04	577	698	760.5	707.30
ENSG00000000938	1.11	0.48	1.05	0.87	0	0	0.0	2.35

Accessing Count Data:

Raw Counts:

counts(dds, normalized = FALSE)

	LNCAP_Hypoxia_S1	LNCAP_Hypoxia_S2	LNCAP_Normoxia_S1	LNCAP_Normoxia_S2	PC3_Hypoxia_S1	PC3_Hypoxia_S2	PC3_Normoxia_S1	PC3_Normoxia_S2
ENSG00000000003	694	652	366	332	1087	384	336	865
ENSG00000000005	0	0	0	0	0	0	0	0
ENSG00000000419	4317	4761	4969	5939	3127	995	962	2374
ENSG00000000457	725	789	566	648	99	30	30	94
ENSG00000000460	351	393	419	407	454	206	258	601
ENSG00000000938	2	1	2	2	0	0	0	2

Normalized Counts:

counts(dds, normalized = TRUE)

	LNCAP_Hypoxia_S1	LNCAP_Hypoxia_S2	LNCAP_Normoxia_S1	LNCAP_Normoxia_S2	PC3_Hypoxia_S1	PC3_Hypoxia_S2	PC3_Normoxia_S1	PC3_Normoxia_S2
ENSG00000000003	385.26	313.23	192.03	144.41	1382	1302	990.4	1017.99
ENSG00000000005	0.00	0.00	0.00	0.00	0	0	0.0	0.00
ENSG00000000419	2396.52	2287.27	2607.16	2583.34	3975	3373	2835.6	2793.88
ENSG00000000457	402.47	379.05	296.97	281.87	126	102	88.4	110.63
ENSG00000000460	194.85	188.81	219.84	177.04	577	698	760.5	707.30
ENSG00000000938	1.11	0.48	1.05	0.87	0	0	0.0	2.35

Size Factors:

sizeFactors(dds)

#>  LNCAP_Hypoxia_S1  LNCAP_Hypoxia_S2 LNCAP_Normoxia_S1 LNCAP_Normoxia_S2    PC3_Hypoxia_S1    PC3_Hypoxia_S2 
#>             1.801             2.082             1.906             2.299             0.787             0.295 
#>   PC3_Normoxia_S1   PC3_Normoxia_S2 
#>             0.339             0.850

Effects of Normalization:

Value Scaling
- Raw counts transformed to comparable scales
- Accounts for sequencing depth differences
- Enables direct sample comparisons
Expression Patterns
- Relative expression levels preserved
- Zero counts remain zero
- Non-zero counts adjusted proportionally
Notable Examples
- ENSG00000000419: High expression, consistent across samples
- ENSG00000000005: No expression in any sample
- ENSG00000000003: Variable expression between cell lines

These normalized counts provide a foundation for: - Comparing expression between samples - Visualizing gene expression patterns - Identifying differentially expressed genes

5 🏷️ Adding Gene Annotations

5.1 🔍 Using BioMart for Gene Information

To make our analysis more interpretable, we’ll convert Ensembl IDs to common gene names using BioMart(Durinck et al. 2009):

BioMart Data Export Steps:

Access BioMart
- Visit Ensembl BioMart
- Select “Ensembl Genes 114”
Choose Dataset
- Database: Human Genes (GRCh38.p14)
- Ensure matching genome version
Select Attributes
- ✓ Gene stable ID (ENSG00000111640)
- ✓ Gene name (GAPDH)
- ✓ Gene type (for pathway analysis)
Export Settings
- Format: CSV
- Option: “Unique results only”
- File: “GRCh38.p14_annotation.csv”

5.2 📥 Loading Annotations

Import and examine the annotation data:

# Read annotation file
annotation <- read.csv(
  "reference_genomes/GRCh38.p14_annotation.csv", 
  header = TRUE, 
  stringsAsFactors = FALSE
)

# Preview annotations
head(annotation)

Annotation Information: - Gene stable ID: Unique Ensembl identifier - Gene name: Common gene symbol - Gene type: Biological classification

5.3 🔄 Combining Expression and Annotation Data

We’ll merge the normalized counts with gene annotations using tidyverse functions:

# Convert normalized counts to data frame with Ensembl IDs
normalized_counts <- rownames_to_column(
  as.data.frame(normalized_counts), 
  var = "ensembl_id"
)

# Join annotations with expression data
annotated_data <- right_join(
  annotation, 
  normalized_counts, 
  by = c("Gene.stable.ID" = "ensembl_id")
)

# Preview merged data
head(annotated_data)

# Save annotated results
write.csv(
  annotated_data, 
  file = "results/gene_annotated_normalized_counts.csv",
  row.names = FALSE
)

Features of Merged Dataset:

Comprehensive Information
- Gene identifiers (Ensembl ID)
- Common gene names
- Gene biotypes
- Normalized expression values
Example Insights
- MT-RNR1/2: Highly expressed mitochondrial rRNAs
- MT-TF/TV: Low expression tRNAs
- MT-ND1: Moderate expression protein-coding gene
Data Structure
- Organized by gene
- Expression values by condition
- Ready for downstream analysis

The annotated expression data provides several advantages: - Easy gene lookup by symbol - Biological context through gene types - Normalized values for direct comparisons - Export-ready format for collaboration

Next, we’ll explore visualizations of this data to understand expression patterns and identify differentially expressed genes.

6 🔍 Quality Control and Exploratory Analysis

6.1 📊 Sample Variability Assessment

Before proceeding with differential expression analysis, it’s crucial to assess sample-to-sample variability to: - Identify potential outliers - Validate experimental design - Confirm replicate similarity - Detect batch effects

Key Questions to Address: 1. How similar are biological replicates? 2. Do samples cluster by experimental condition? 3. Are there any obvious batch effects? 4. Which genes show high variability?

6.1.1 🔄 Data Transformation

We’ll use variance stabilizing transformation (VST) to prepare data for visualization:

# Apply variance stabilizing transformation
vsd <- vst(dds, blind = TRUE)

# Confirm transformation
print(sprintf("Number of genes transformed: %d", nrow(vsd)))

#> [1] "Number of genes transformed: 60676"

💡 Why VST? - Reduces mean-dependent variance - Makes data more suitable for visualization - Enables comparison across wide expression ranges - Preserves relative differences between samples

6.1.2 📊 Sample Distance Analysis

We’ll create a heatmap of sample-to-sample distances to visualize relationships between samples:

Method Overview: 1. Calculate Euclidean distances between samples 2. Create distance matrix 3. Perform hierarchical clustering 4. Visualize as heatmap

# Function to plot sample distances
plotDists <- function(vsd.obj) {
  # Calculate sample distances
  sampleDists <- dist(t(assay(vsd.obj)))
  sampleDistMatrix <- as.matrix(sampleDists)
  
  # Add condition labels
  rownames(sampleDistMatrix) <- paste(vsd.obj$condition)
  
  # Create color palette
  colors <- colorRampPalette(
    rev(RColorBrewer::brewer.pal(9, "Blues"))
  )(255)
  
  # Generate heatmap
  pheatmap::pheatmap(
    sampleDistMatrix,
    clustering_distance_rows = sampleDists,
    clustering_distance_cols = sampleDists,
    col = colors,
    main = "Sample Distance Matrix",
    fontsize = 15,
    border_color = NA
  )
}

# Generate and save plots
p1 <- plotDists(vsd)
p1

Figure 3: Hierarchical clustering of sample-to-sample distances

Key Observations:

Primary Clustering
- Clear separation by cell type (LNCaP vs PC3)
- Strongest factor in sample variation
Secondary Clustering
- Samples group by treatment (hypoxia vs normoxia)
- Consistent within cell types
Replicate Similarity
- Biological replicates cluster together
- Indicates good experimental reproducibility
Quality Validation
- Expected hierarchical grouping
- No obvious outliers
- Confirms sample quality

The clustering patterns validate our experimental design and suggest high-quality data suitable for differential expression analysis.

6.1.3 🔍 Analysis of Highly Variable Genes

Examining the most variable genes helps identify key drivers of sample differences and potential markers:

Analysis Strategy: 1. Calculate gene-wise variance 2. Select top variable genes 3. Center expression values 4. Create annotated heatmap 5. Identify expression patterns

# Function to create variable gene heatmap
variable_gene_heatmap <- function(vsd.obj, num_genes = 500, annotation, title = "") {
  # Set up color scheme
  brewer_palette <- "RdBu"
  ramp <- colorRampPalette(RColorBrewer::brewer.pal(11, brewer_palette))
  mr <- ramp(256)[256:1]
  
  # Process expression data
  stabilized_counts <- assay(vsd.obj)
  row_variances <- rowVars(stabilized_counts)
  
  # Select top variable genes
  top_variable_genes <- stabilized_counts[
    order(row_variances, decreasing=TRUE)[1:num_genes],
  ]
  
  # Center expression values
  top_variable_genes <- top_variable_genes - 
    rowMeans(top_variable_genes, na.rm=TRUE)
  
  # Map to gene symbols
  gene_names <- annotation$Gene.name[
    match(rownames(top_variable_genes), annotation$Gene.stable.ID)
  ]
  rownames(top_variable_genes) <- gene_names
  
  # Prepare sample annotations
  coldata <- as.data.frame(vsd.obj@colData)
  coldata$sizeFactor <- NULL
  
  # Generate heatmap
  pheatmap::pheatmap(
    top_variable_genes,
    color = mr,
    annotation_col = coldata,
    fontsize_col = 8,
    fontsize_row = 250/num_genes,
    border_color = NA,
    main = title,
    show_rownames = TRUE,
    cluster_cols = TRUE,
    cluster_rows = TRUE,
    clustering_method = "complete",
    clustering_distance_rows = "euclidean",
    clustering_distance_cols = "euclidean"
  )
}

# Generate and save heatmap
p2 <- variable_gene_heatmap(
  vsd, 
  num_genes = 40, 
  annotation = annotation,
  title = "Top 40 Most Variable Genes"
)
p2

$Variable Genes Heatmap$

Figure 4: Heatmap of top 40 most variable genes

Key Patterns:

Cell Line-Specific Expression
- Distinct gene sets for LNCaP vs PC3
- Clear molecular signatures
- Strong cell type identity
Treatment Effects
- Secondary clustering by hypoxia/normoxia
- Consistent response patterns
- Cell type-specific responses
Replicate Consistency
- Similar patterns within conditions
- Good technical reproducibility
- Minimal batch effects
Data Quality Indicators
- Clear separation of conditions
- Expected biological patterns
- No anomalous clusters

Biological Implications:

Cell Line Differences
- Expected clear clustering for cell lines
- Reflects distinct cancer subtypes
- Validates experimental design
Sample Heterogeneity
- Low variability within cell lines
- More variation expected in tissue samples
- Useful for quality control
Applications
- Cell line authentication
- Contamination detection
- Batch effect assessment

The distinct clustering patterns confirm the quality of our experimental design and data, providing a solid foundation for differential expression analysis.

6.1.4 📈 Principal Component Analysis

Principal Component Analysis (PCA) helps visualize the major sources of variation in our dataset:

PCA Benefits: - Reduces data dimensionality - Identifies major variance sources - Reveals sample relationships - Highlights potential batch effects

# Function to create PCA plot
plot_PCA <- function(vsd.obj) {
  # Generate PCA data
  pcaData <- plotPCA(vsd.obj, 
                     intgroup = c("condition"), 
                     returnData = TRUE)
  
  # Calculate variance percentages
  percentVar <- round(100 * attr(pcaData, "percentVar"))
  
  # Create enhanced PCA plot
  ggplot(pcaData, aes(PC1, PC2, color = condition)) +
    # Add points with larger size
    geom_point(size = 8, alpha = 0.8) +
    
    # Add labels with repulsion
    ggrepel::geom_text_repel(
      aes(label = name),
      color = "black",
      size = 3,
      box.padding = 0.5,
      point.padding = 0.5
    ) +
    
    # Customize theme
    theme_bw() +
    theme(
      legend.position = "right",
      panel.grid.major = element_line(color = "grey90"),
      panel.grid.minor = element_blank(),
      axis.text = element_text(size = 10),
      axis.title = element_text(size = 12, face = "bold"),
      legend.title = element_text(size = 11, face = "bold"),
      legend.text = element_text(size = 10),
      plot.title = element_text(size = 14, face = "bold", hjust = 0.5)
    ) +
    
    # Add labels and title
    labs(
      x = paste0("PC1: ", percentVar[1], "% variance"),
      y = paste0("PC2: ", percentVar[2], "% variance"),
      title = "Principal Component Analysis",
      color = "Condition"
    )
}

# Generate and save PCA plot
p3 <- plot_PCA(vsd)
p3

Figure 5: PCA plot

🔍 Key Observations:

** Primary Variation (PC1: ~99%)**
- Dominant separation by cell type
- LNCaP vs PC3 is major source of variance
- Clear biological distinction
Secondary Variation (PC2)
- Separates hypoxia vs normoxia
- Smaller effect than cell type
- Consistent within cell lines
Biological Implications
- Cell type identity is preserved
- Hypoxia response is cell type-specific
- Good experimental separation

📋 Recommended Analysis Strategy:

Given the strong cell type effect, we should: 1. Separate LNCaP and PC3 samples 2. Create individual DESeq2 objects per cell line 3. Analyze hypoxia effects independently 4. Compare responses between cell lines

This approach will help isolate treatment effects from the overwhelming cell type differences.

7 🧫 Cell Line-Specific Analysis

7.1 📋 Overview

Key Points: - PCA reveals cell type as dominant source of variation - Separate analyses needed for unbiased hypoxia response assessment - Cell-specific analysis enables identification of: - Common hypoxia response genes - Cell type-specific responses - Potential therapeutic targets

7.2 📊 Analysis Strategy

Based on our exploratory analysis, we’ll employ a systematic approach:

1. Data Separation - Create cell line-specific datasets - Preserve biological replicates - Maintain data structure

2. Independent Analysis - Generate cell-specific DESeq2 objects - Perform normalized analyses - Control for technical factors

3. Response Comparison - Identify shared responses - Highlight cell-specific patterns - Quantify effect sizes

4. Biological Integration - Pathway analysis by cell type - Mechanistic insights - Therapeutic implications

7.3 🔬 Cell Line Data Preparation

We’ll create a robust function for generating cell-specific DESeq2 objects:

#' Create Cell-Specific DESeq2 Object
#'
#' This function creates a DESeq2 object for analyzing differential expression
#' within a specific cell line. It handles data subsetting, quality control,
#' and DESeq2 initialization.
#'
#' @param my_data Count matrix with genes as rows and samples as columns
#' @param groups Character vector of length 2 specifying conditions to compare
#' @return A DESeq2 object ready for differential expression analysis
generate_DESeq_object <- function(my_data, groups) {
  # Input validation
  if (length(groups) != 2) {
    stop("Exactly two groups must be provided for comparison")
  }
  
  # Extract and validate data for each group
  data_subset1 <- my_data[, grep(str_c("^", groups[1]), colnames(my_data))]
  data_subset2 <- my_data[, grep(str_c("^", groups[2]), colnames(my_data))]
  
  if (ncol(data_subset1) == 0 || ncol(data_subset2) == 0) {
    stop("No samples found for one or both groups")
  }
  
  # Combine data
  my_countData <- cbind(data_subset1, data_subset2)
  
  # Create condition vector with informative names
  condition <- c(
    rep(groups[1], ncol(data_subset1)), 
    rep(groups[2], ncol(data_subset2))
  )
  
  # Create comprehensive sample metadata
  my_colData <- data.frame(
    condition = factor(condition),
    cell_line = factor(str_extract(condition, "^[^_]+")),
    treatment = factor(str_extract(condition, "[^_]+$")),
    row.names = colnames(my_countData)
  )
  
  # Create DESeq2 object with quality filters
  dds <- DESeqDataSetFromMatrix(
    countData = my_countData,
    colData = my_colData,
    design = ~ condition
  )
  
  # Filter low-count genes
  keep <- rowSums(counts(dds)) >= 10
  dds <- dds[keep,]
  message(sprintf("\nRetained %d genes after filtering", nrow(dds)))
  
  # Run DESeq2 analysis
  message("\nRunning DESeq2 analysis...")
  dds <- DESeq(dds, quiet = TRUE)
  
  # Report completion
  message("Analysis complete!")
  
  return(dds)
}

Let’s create cell-specific DESeq2 objects and validate their structure:

# Create LNCaP-specific object
lncap <- generate_DESeq_object(
  data_round, 
  c("LNCAP_Hypoxia", "LNCAP_Normoxia")
)

# Create PC3-specific object
pc3 <- generate_DESeq_object(
  data_round, 
  c("PC3_Hypoxia", "PC3_Normoxia")
)

# Perform variance stabilizing transformation
lncap_vsd <- vst(lncap, blind = TRUE)
pc3_vsd <- vst(pc3, blind = TRUE)

Quality Control Checks: 1. Sample metadata correctly assigned 2. Low-count genes filtered out 3. Normalization factors calculated 4. Design formula properly specified

7.4 🔬 Analyzing Cell-Specific Responses

Now we’ll examine hypoxia response patterns within each cell line:

7.4.1 📊 Visualizing Cell-Specific Expression Patterns

We’ll analyze the most variable genes in each cell line to understand their distinct responses to hypoxia:

# Generate LNCaP variable genes heatmap
p4 <- variable_gene_heatmap(
  lncap_vsd, 
  num_genes = 30, 
  annotation = annotation, 
  title = "LNCaP Hypoxia Response Genes"
)

We observe that the LNCaP variable genes are enriched in genes that are specific to the LNCaP cell line, such as KLK3 (PSA) and KLK2. The PC3 variable genes are enriched in genes that are specific to the PC3 cell line, such as TMPRSS2 and SLC45A3.

# Generate PC3 variable genes heatmap

p5 <- variable_gene_heatmap(pc3_vsd, 30, annotation = annotation, title = "PC3 variable genes")
p6 <- gridExtra::grid.arrange(p4[[4]],p5[[4]], nrow = 1)
p6

Figure 6: LNCaP_PC3_variable_genes plot

This is a vastly different set of genes than what was observed when we had samples from both cell lines combined. Our differential gene expression analysis should now yield a greater depth of genes for the comparison we care about: hypoxia vs normoxia.

7.5 📊 Differential Expression Results

Extract differentially expressed genes between hypoxia and normoxia conditions using DESeq2’s results() function:

results(lncap, contrast = c("condition", "LNCAP_Hypoxia", "LNCAP_Normoxia"))

#> log2 fold change (MLE): condition LNCAP_Hypoxia vs LNCAP_Normoxia 
#> Wald test p-value: condition LNCAP_Hypoxia vs LNCAP_Normoxia 
#> DataFrame with 23422 rows and 6 columns
#>                  baseMean log2FoldChange     lfcSE      stat       pvalue        padj
#>                 <numeric>      <numeric> <numeric> <numeric>    <numeric>   <numeric>
#> ENSG00000000003   518.727      1.0693831  0.256250  4.173203 0.0000300347 0.000328262
#> ENSG00000000419  4939.277     -0.1333728  0.120121 -1.110321 0.2668606680 0.470253565
#> ENSG00000000457   681.180      0.4480815  0.182940  2.449339 0.0143118852 0.056207398
#> ENSG00000000460   390.541     -0.0330573  0.243049 -0.136011 0.8918125879 0.949226832
#> ENSG00000001036   398.773     -0.6328722  0.298934 -2.117099 0.0342514141 0.108988224
#> ...                   ...            ...       ...       ...          ...         ...
#> ENSG00000288573  11.07937     -0.6620047  1.225872 -0.540027    0.5891781    0.765965
#> ENSG00000288582   3.29568     -1.0474228  2.025291 -0.517172    0.6050364          NA
#> ENSG00000288585  70.09038     -0.9664820  0.517927 -1.866057    0.0620334    0.169749
#> ENSG00000288586  77.58432     -0.6772347  0.455622 -1.486398    0.1371740    0.300559
#> ENSG00000288587  16.53520     -0.0978059  0.910850 -0.107379    0.9144885    0.960657

head(as.data.frame(results(lncap, contrast = c("condition", "LNCAP_Hypoxia", "LNCAP_Normoxia"))))

Key statistics from DESeq2 results: - baseMean: Overall gene expression level - log2FoldChange: Expression difference between conditions (negative = higher in normoxia) - padj: Adjusted p-value for significance

Filtering criteria: 1. Statistical significance: padj < cutoff 2. Expression change: Sort by log2FoldChange 3. Optional: Filter low-expressed genes using baseMean or CPM 4. Generate ranked list (.rnk) for enrichment analysis

Since there are many variations of sorting / filtering the data, I wrote the function generate_DE_results() to perform multiple filtering steps and provide output in the form of csv files. I export these files so they can be easily shared and interpreted by other bench scientists without having to read the data into R.

generate_DE_results <- function (dds, comparisons, padjcutoff = 0.001, log2cutoff = 0.5, cpmcutoff = 2) {
  # generate average counts per million metric from raw count data 
  raw_counts <- counts(dds, normalized = F)
  cpms <- enframe(rowMeans(edgeR::cpm(raw_counts)))
  colnames(cpms) <- c("ensembl_id", "avg_cpm")
  
  # extract DESeq results between the comparisons indicated
  res <- results(dds, contrast = c("condition", comparisons[1], comparisons[2]))[,-c(3,4)]
  
  # annotate the data with gene name and average counts per million value
  res <- as_tibble(res, rownames = "ensembl_id")
  # read in the annotation and append it to the data
  my_annotation <- read.csv("reference_genomes/GRCh38.p14_annotation.csv", header = TRUE, stringsAsFactors = F)
  res <- left_join(res, my_annotation, by = c("ensembl_id" = "Gene.stable.ID"))
  # append the average cpm value to the results data
  res <- left_join(res, cpms, by = c("ensembl_id" = "ensembl_id"))
  
  # combine normalized counts with entire DE list
  normalized_counts <- round(counts(dds, normalized = TRUE),3)
  pattern <- str_c(comparisons[1], "|", comparisons[2])
  combined_data <- as_tibble(cbind(res, normalized_counts[,grep(pattern, colnames(normalized_counts))] ))
  combined_data <- combined_data[order(combined_data$log2FoldChange, decreasing = TRUE),]
  
  # make ordered rank file for GSEA, selecting only protein coding genes
  res_prot <- res[which(res$Gene.type == "protein_coding"),]
  res_prot_ranked <- res_prot[order(res_prot$log2FoldChange, decreasing = TRUE),c("Gene.name", "log2FoldChange")]
  res_prot_ranked <- na.omit(res_prot_ranked)
  res_prot_ranked$Gene.name <- str_to_upper(res_prot_ranked$Gene.name)
  
  # generate sorted lists with the indicated cutoff values
  res <- res[order(res$log2FoldChange, decreasing=TRUE ),]
  de_genes_padj <- res[which(res$padj < padjcutoff),]
  de_genes_log2f <- res[which(abs(res$log2FoldChange) > log2cutoff & res$padj < padjcutoff),]
  de_genes_cpm <- res[which(res$avg_cpm > cpmcutoff & res$padj < padjcutoff),]
  
  # write output to files
  write.csv (de_genes_padj, file = paste0("results/", comparisons[1], "_vs_", comparisons[2], "_padj_cutoff.csv"), row.names = FALSE)
  write.csv (de_genes_log2f, file = paste0("results/", comparisons[1], "_vs_", comparisons[2], "_log2f_cutoff.csv"), row.names = FALSE)
  write.csv (de_genes_cpm, file = paste0("results/", comparisons[1], "_vs_", comparisons[2], "_cpm_cutoff.csv"), row.names = FALSE)
  write.csv (combined_data, file = paste0("results/", comparisons[1], "_vs_", comparisons[2], "_allgenes.csv"), row.names = FALSE)
  write.table (res_prot_ranked, file = paste0("results/", comparisons[1], "_vs_", comparisons[2], "_rank.rnk"), sep = "\t", row.names = F, quote = F)
  
  writeLines( paste0("For the comparison: ", comparisons[1], "_vs_", comparisons[2], ", out of ", nrow(combined_data), " genes, there were: \n", 
               nrow(de_genes_padj), " genes below padj ", padjcutoff, "\n",
               nrow(de_genes_log2f), " genes below padj ", padjcutoff, " and above a log2FoldChange of ", log2cutoff, "\n",
               nrow(de_genes_cpm), " genes below padj ", padjcutoff, " and above an avg cpm of ", cpmcutoff, "\n",
               "Gene lists ordered by log2fchange with the cutoffs above have been generated.") )
  gene_count <- tibble (cutoff_parameter = c("padj", "log2fc", "avg_cpm" ), 
                        cutoff_value = c(padjcutoff, log2cutoff, cpmcutoff), 
                        signif_genes = c(nrow(de_genes_padj), nrow(de_genes_log2f), nrow(de_genes_cpm)))
  invisible(gene_count)
}

After running the function, the console should display the number of genes that passed each filtering critera:

lncap_output <- generate_DE_results (lncap, c("LNCAP_Hypoxia", "LNCAP_Normoxia"))
pc3_output <- generate_DE_results(pc3, c("PC3_Hypoxia", "PC3_Normoxia"))

#> For the comparison: LNCAP_Hypoxia_vs_LNCAP_Normoxia, out of 23422 genes, there were: 
#> 1984 genes below padj 0.001
#> 1963 genes below padj 0.001 and above a log2FoldChange of 0.5
#> 1875 genes below padj 0.001 and above an avg cpm of 2
#> Gene lists ordered by log2fchange with the cutoffs above have been generated.
#> For the comparison: PC3_Hypoxia_vs_PC3_Normoxia, out of 20576 genes, there were: 
#> 765 genes below padj 0.001
#> 765 genes below padj 0.001 and above a log2FoldChange of 0.5
#> 653 genes below padj 0.001 and above an avg cpm of 2
#> Gene lists ordered by log2fchange with the cutoffs above have been generated.

We should also see a set of csv files show up in the same folder as the R script. We can read in the output files with read.csv() and observe what the file structure is like:

res <- read.csv("results/LNCAP_Hypoxia_vs_LNCAP_Normoxia_allgenes.csv", header = TRUE)
head(res)

The _allgenes.csv file will be used in the next section for visualizations. It is an aggregate of all the data we have curated so far: the results() output, the gene annotations, and the normalized counts for every gene. We can further organize/filter the data for the visualizations based on user preferences. The other csv files, such as _padj_cutoff.csv, can be distributed to researchers who are only interested in the gene lists.

7.6 📊 Results Visualization

Visualize differential expression results to examine: 1. Expression patterns of specific genes 2. Sample variability and top DEGs 3. Distribution of up/down regulated genes 4. Shared DEGs between comparisons 5. Pathway enrichment analysis

7.6.1 📈 PlotCounts

We are often interested in plotting the normalized counts for certain genes of interest, in order to get a sense of the spread of the data and how genes are expressed across each of the groups. This is especially of interest for genes that show up as differentially expressed in our DESeq2 results. A way to plot the normalized counts for each sample is by using the built-in DESeq2::plotCounts() function. However, this function leaves a lot to be desired. As an example, I plot the gene IGFBP1 (a hypoxia-inducible gene) using its Ensembl ID below.

plotCounts(dds, gene="ENSG00000146678", intgroup="condition")

As you can see, the sample names aren’t even all displayed if their names are too long. We can instead use ggplot2 to graph the data, by extracting the normalized counts using returnData = TRUE:

d <- plotCounts(dds, gene="ENSG00000146678", intgroup="condition", returnData=TRUE)

p7 <- ggplot(d, aes(x=condition, y=count)) + 
  geom_point(size = 10, position=position_jitter(w=0.4,h=0), aes (color = condition)) +   scale_y_log10(breaks=c(25,100,400))
p7

Figure 7: ENSG00000146678 plot

However, this is still very frustrating to work with, since we have to input the Ensembl ID instead of a gene name. Researchers typically want to search by gene name, and this function forces us to look up the Ensembl ID each time. To solve this problem, I wrote my own function plot_counts() to accept either the gene name, Ensembl ID, or an index such as which.min(res$padj). I also provide two ways to normalize the data: by counts per million (cpm) or using the built-in DESeq2 normalized counts.

plot_counts <- function (dds, gene, normalization = "DESeq2"){
  # read in the annotation file
  annotation <- read.csv("reference_genomes/GRCh38.p14_annotation.csv", header = TRUE, stringsAsFactors = F)
  # obtain normalized data
  if (normalization == "cpm") {
    normalized_data <- cpm(counts(dds, normalized = F)) # normalize the raw data by counts per million
  } else if (normalization == "DESeq2")
    normalized_data <- counts(dds, normalized = TRUE) # use DESeq2 normalized counts
  # get sample groups from colData
  condition <- dds@colData$condition
  # get the gene name from the ensembl id
  if (is.numeric(gene)) { # check if an index is supplied or if ensembl_id is supplied
    if (gene%%1==0 )
      ensembl_id <- rownames(normalized_data)[gene]
    else
      stop("Invalid index supplied.")
  } else if (gene %in% annotation$Gene.name){ # check if a gene name is supplied
    ensembl_id <- annotation$Gene.stable.ID[which(annotation$Gene.name == gene)]
  } else if (gene %in% annotation$Gene.stable.ID){
    ensembl_id <- gene
  } else {
    stop("Gene not found. Check spelling.")
  }
  expression <- normalized_data[ensembl_id,]
  gene_name <- annotation$Gene.name[which(annotation$Gene.stable.ID == ensembl_id)]
  # construct a tibble with the grouping and expression
  gene_tib <- tibble(condition = condition, expression = expression)
  ggplot(gene_tib, aes(x = condition, y = expression))+
    geom_boxplot(aes(fill = condition), outlier.size = NULL)+
    geom_point(aes(color = condition))+
    labs (title = paste0("Expression of ", gene_name, " - ", ensembl_id), x = "group", y = paste0("Normalized expression (", normalization , ")"))+
    theme(axis.text.x = element_text(size = 11), axis.text.y = element_text(size = 11))
}

p8 <- plot_counts(dds, "IGFBP1")
p8

Figure 8: plot_counts

We observe that IGFBP1 is elevated in the the hypoxia conditions relative to the respective normoxia controls. Interestingly, the upregulation of IGFBP1 is much higher in PC3 cells than in LNCaP cells. This is a nice example of how this plot is useful to determine relative expression levels between samples.

7.6.2 🌡️ Differential gene heatmap

The most common heatmap used to display RNA-seq results is the differential gene heatmap. This is a heatmap where the normalized values for each sample are plotted for the top most upregulated genes which are significantly different between the comparison groups. Starting from the results object, we first filter the results by padj and then take the genes which have the greatest log2FoldChange value. The normalized counts for each gene is scaled by row (across samples) in order to emphasize the difference between the comparison groups (hypoxia vs normoxia). The purpose of this visualization is to give a sense of how variable the data are from sample to sample, as well as to quickly examine the top differential genes. I wrote the function DE_gene_heatmap() to generate this plot, using pheatmap::pheatmap() with the scale = "row" argument.

res <- read.csv ("results/LNCAP_Hypoxia_vs_LNCAP_Normoxia_allgenes.csv", header = TRUE)

DE_gene_heatmap <- function(res, padj_cutoff = 0.0001, ngenes = 20) {
  # generate the color palette
  brewer_palette <- "RdBu"
  ramp <- colorRampPalette(RColorBrewer::brewer.pal(11, brewer_palette))
  mr <- ramp(256)[256:1]
  # obtain the significant genes and order by log2FoldChange
  significant_genes <- res %>% filter(padj < padj_cutoff) %>% arrange (desc(log2FoldChange)) %>% head (ngenes)
  heatmap_values <- as.matrix(significant_genes[,-c(1:8)])
  rownames(heatmap_values) <- significant_genes$Gene.name
  # plot the heatmap using pheatmap
  pheatmap::pheatmap(heatmap_values, color = mr, scale = "row", fontsize_col = 12, fontsize_row = 200/ngenes, fontsize = 5, border_color = NA)
}
p9 <- DE_gene_heatmap(res, 0.001, 30)
p9

Figure 9: DE_gene_heatmap plot

We observe that expression of the differential genes is very consistent within groups. We are also able to identify that LNCAP_Hypoxia_S1 has higher expression of the top ~10 genes than LNCAP_Hypoxia_S2.

7.6.3 🌋 Volcano Plot Analysis

The volcano plot is a powerful visualization that combines statistical significance with magnitude of change, allowing us to identify genes with both large fold changes and high statistical confidence. In this plot:

X-axis: log2FoldChange shows the magnitude and direction of expression changes
Y-axis: -log10(padj) represents statistical significance
Each point represents one gene
Upregulated genes appear on the right side (positive log2FC)
Downregulated genes appear on the left side (negative log2FC)
Higher points indicate greater statistical significance

Interpretation Guide: 1. Top right quadrant: Significantly upregulated genes (high fold change, low p-value) 2. Top left quadrant: Significantly downregulated genes (negative fold change, low p-value) 3. Center/bottom: Genes with minimal changes or insufficient statistical confidence

Let’s create an enhanced volcano plot for the LNCaP hypoxia vs normoxia comparison:

# Read the results file
res <- read.csv("results/LNCAP_Hypoxia_vs_LNCAP_Normoxia_allgenes.csv", header = TRUE)

plot_volcano <- function(res, padj_cutoff = 0.05, fc_cutoff = 1, nlabel = 10, label.by = "padj") {
  # Create significance categories
  res <- res %>%
    mutate(
      expression = case_when(
        log2FoldChange >= fc_cutoff & padj < padj_cutoff ~ "Upregulated",
        log2FoldChange <= -fc_cutoff & padj < padj_cutoff ~ "Downregulated",
        TRUE ~ "Not Significant"
      )
    ) %>%
    filter(!is.na(padj))  # Remove NA values
    
  # Prepare genes for labeling
  if (label.by == "padj") {
    up_genes <- res %>% 
      filter(expression == "Upregulated") %>% 
      arrange(padj) %>% 
      head(nlabel)
    down_genes <- res %>% 
      filter(expression == "Downregulated") %>% 
      arrange(padj) %>% 
      head(nlabel)
  } else if (label.by == "log2FoldChange") {
    up_genes <- res %>% 
      filter(expression == "Upregulated") %>% 
      arrange(desc(abs(log2FoldChange))) %>% 
      head(nlabel)
    down_genes <- res %>% 
      filter(expression == "Downregulated") %>% 
      arrange(desc(abs(log2FoldChange))) %>% 
      head(nlabel)
  }
  
  # Generate statistics for title
  stats <- list(
    total = nrow(res),
    up = sum(res$expression == "Upregulated"),
    down = sum(res$expression == "Downregulated")
  )
  
  # Create enhanced volcano plot
  ggplot(res, aes(x = log2FoldChange, y = -log10(padj))) +
    # Add points
    geom_point(aes(color = expression), alpha = 0.7, size = 1) +
    # Color scheme
    scale_color_manual(
      values = c(
        "Upregulated" = "#fc8d59",
        "Downregulated" = "#91bfdb",
        "Not Significant" = "grey80"
      )
    ) +
    # Add gene labels with repulsion
    ggrepel::geom_text_repel(
      data = up_genes,
      aes(label = Gene.name),
      size = 3,
      color = "#d73027",
      max.overlaps = Inf,
      box.padding = 0.5
    ) +
    ggrepel::geom_text_repel(
      data = down_genes,
      aes(label = Gene.name),
      size = 3,
      color = "#4575b4",
      max.overlaps = Inf,
      box.padding = 0.5
    ) +
    # Add threshold lines
    geom_vline(xintercept = c(-fc_cutoff, fc_cutoff), linetype = "dashed", color = "grey50", alpha = 0.5) +
    geom_hline(yintercept = -log10(padj_cutoff), linetype = "dashed", color = "grey50", alpha = 0.5) +
    # Customize theme
    theme_minimal() +
    theme(
      legend.position = "right",
      panel.grid.minor = element_blank(),
      plot.title = element_text(size = 12),
      plot.subtitle = element_text(size = 10)
    ) +
    # Add labels
    labs(
      title = "Differential Expression Analysis",
      subtitle = sprintf("Total: %d genes | Up: %d | Down: %d (padj < %.2g, |log2FC| > %.1f)",
                        stats$total, stats$up, stats$down, padj_cutoff, fc_cutoff),
      x = "log2 Fold Change",
      y = "-log10(adjusted p-value)",
      color = "Expression Change"
    )
}

# Generate volcano plots with different thresholds and labeling strategies
# 1. Default stringent analysis
p10 <- plot_volcano(res, 
                   padj_cutoff = 0.001,  # Stringent significance threshold
                   fc_cutoff = 1.5,      # Require at least 2.8-fold change
                   nlabel = 10,          # Label top 10 genes
                   label.by = "padj")    # Label most significant genes
p10

Figure 10: Volcano plot_1(padj_cutoff = 0.001)

# 2. Alternative analysis focusing on fold change
p11 <- plot_volcano(res, 
                   padj_cutoff = 0.05,   # More permissive significance
                   fc_cutoff = 2,        # Focus on genes with larger changes
                   nlabel = 15,          # Label more genes
                   label.by = "log2FoldChange")  # Label genes with largest changes
p11

Figure 11: Volcano plot_2(padj_cutoff = 0.05)

7.6.4 🌋 Enhanced volcano plots(Blighe K 2025)

# Create a column for gene names from the rownames
res$gene_names <- res$Gene.name

# Sort the results by padj and then by log2FoldChange
# This is a good way to find the most significant and most-changed genes
sorted_res <- res[order(res$padj, abs(res$log2FoldChange), decreasing = c(FALSE, TRUE)), ]

# Filter for significantly upregulated genes
top_up_genes <- subset(sorted_res, log2FoldChange > 2 & padj < 0.05)

# Filter for significantly downregulated genes
top_down_genes <- subset(sorted_res, log2FoldChange < -2 & padj < 0.05)

# Select the top 10 gene names from each list
select_lab_list <- c(head(top_up_genes, 10)$gene_names, head(top_down_genes, 10)$gene_names)

# Create the enhanced volcano plot
p13 <- EnhancedVolcano(res,
                        lab = res$gene_name, # Use the column with gene names
                        x = 'log2FoldChange',
                        y = 'padj',
                        ylab = bquote(~-Log[10] ~ italic(FDR)),
                        pCutoff = 0.05,
                        FCcutoff = 2,
                        selectLab = select_lab_list, # Add this line
                        legendLabels=c('Not DE &\nabsolute FC < 2',
                        'Not DE &\nabsolute FC > 2',
                        'FDR < 0.05 &\nabsolute FC < 2',
                        'FDR < 0.05 &\nabsolute FC > 2'
                        ),
                        legendPosition = 'right')

p13

Figure 12: Volcanoplot-enhanced

The downregulated genes are labeled in a different color compared to the upregulated genes. We observe that there are a lot more significantly upregulated genes than there are downregulated genes. Given that we are looking for gene expression changes in cells grown in hypoxic conditions compared to normoxic conditions, this suggests that cells may be ramping up transcription of response pathways.

Key Findings from Volcano Analysis:

Stringent Analysis (p10):
- Focuses on high-confidence changes (padj < 0.001)
- Reveals core hypoxia response genes
- Highlights most statistically significant changes
Fold Change Analysis (p11):
- Captures broader expression changes
- Identifies genes with dramatic expression shifts
- May reveal biologically relevant genes with larger variance

7.6.5 💾 Save processed results for further analysis

# Save results with detailed categorization
res_processed <- res %>%
  mutate(
    expression_category = case_when(
      log2FoldChange >= 2 & padj <= 0.001 ~ "Strongly Upregulated",
      log2FoldChange >= 1 & padj <= 0.05 ~ "Upregulated",
      log2FoldChange <= -2 & padj <= 0.001 ~ "Strongly Downregulated",
      log2FoldChange <= -1 & padj <= 0.05 ~ "Downregulated",
      TRUE ~ "Not Significant"
    )
  )

# Save results for downstream analysis
saveRDS(res_processed, "results/processed_differential_expression.rds")
write.csv(res_processed, "results/processed_differential_expression.csv", row.names = FALSE)

7.7 📈 LogFoldChange comparison plot

Given that we have performed differential gene expression analysis on two different cell lines, we would like to compare the two lines in terms of how their gene expression responds to hypoxia. We want to answer questions such as:

Are the genes that are induced by hypoxia similar in both cell lines?
Which significant genes are shared by both cell lines and which genes are unique to each cell line?

To do so, we can take the significant genes from the hypoxia vs normoxia comparison for both lines and generate a scatterplot of their log2FoldChange values using the custom function below, compare_significant_genes(). This function will display the overlap in the gene lists by color coding significant genes that are shared by both cell lines, as well as those that are unique to each individual cell line.

res1 <- read.csv ("results/LNCAP_Hypoxia_vs_LNCAP_Normoxia_allgenes.csv", header = TRUE)
res2 <- read.csv ("results/PC3_Hypoxia_vs_PC3_Normoxia_allgenes.csv", header = TRUE)

compare_significant_genes <- function (res1, res2, padj_cutoff=0.0001, ngenes=250, nlabel=10, samplenames=c("comparison1", "comparison2"), title = "" ) {
  # get list of most upregulated or downregulated genes for each results table
  genes1 <- rbind(head(res1[which(res1$padj < padj_cutoff),], ngenes), tail(res1[which(res1$padj < padj_cutoff),], ngenes))
  genes2 <- rbind(head(res2[which(res2$padj < padj_cutoff),], ngenes), tail(res2[which(res2$padj < padj_cutoff),], ngenes))
 
   # combine the data from both tables
  de_union <- union(genes1$ensembl_id,genes2$ensembl_id)
  res1_union <- res1[match(de_union, res1$ensembl_id),][c("ensembl_id", "log2FoldChange", "Gene.name")]
  res2_union <- res2[match(de_union, res2$ensembl_id),][c("ensembl_id", "log2FoldChange", "Gene.name")]
  combined <- left_join(res1_union, res2_union, by = "ensembl_id", suffix = samplenames )
  
  # identify overlap between genes in both tables
  combined$de_condition <- 1 # makes a placeholder column
  combined$de_condition[which(combined$ensembl_id %in% intersect(genes1$ensembl_id,genes2$ensembl_id))] <- "Significant in Both"
  combined$de_condition[which(combined$ensembl_id %in% setdiff(genes1$ensembl_id,genes2$ensembl_id))] <- paste0("Significant in ", samplenames[1])
  combined$de_condition[which(combined$ensembl_id %in% setdiff(genes2$ensembl_id,genes1$ensembl_id))] <- paste0("Significant in ", samplenames[2])
  combined[is.na(combined)] <- 0
  
  # find the top most genes within each condition to label on the graph
  label1 <- rbind(head(combined[which(combined$de_condition==paste0("Significant in ", samplenames[1])),],nlabel),
                  tail(combined[which(combined$de_condition==paste0("Significant in ", samplenames[1])),],nlabel))
  label2 <- rbind(head(combined[which(combined$de_condition==paste0("Significant in ", samplenames[2])),],nlabel),
                  tail(combined[which(combined$de_condition==paste0("Significant in ", samplenames[2])),],nlabel))
  label3 <- rbind(head(combined[which(combined$de_condition=="Significant in Both"),],nlabel),
                  tail(combined[which(combined$de_condition=="Significant in Both"),],nlabel))
  combined_labels <- rbind(label1,label2,label3)
  
  # plot the genes based on log2FoldChange, color coded by significance
  ggplot(combined, aes_string(x = paste0("log2FoldChange", samplenames[1]), y = paste0("log2FoldChange", samplenames[2]) )) +
      geom_point(aes(color = de_condition), size = 0.7)+
      scale_color_manual(values= c("#00BA38", "#619CFF", "#F8766D", "red"))+
      ggrepel::geom_text_repel(data= combined_labels, aes_string(label=paste0("Gene.name", samplenames[1]), color = "de_condition"), show.legend = F, size=3)+
      geom_vline(xintercept = c(0,0), size = 0.3, linetype = 2)+ 
      geom_hline(yintercept = c(0,0), size = 0.3, linetype = 2)+
      labs(title = title,x = paste0("log2FoldChange in ", samplenames[1]), y = paste0("log2FoldChange in ", samplenames[2]))+
      theme_minimal()+
      theme(legend.title = element_blank())
}

p14 <- compare_significant_genes(res1,res2, samplenames = c("LNCaP", "PC3"), title = "Hypoxia-induced gene expression differences in LNCaP vs PC3 cells") 
p14

$compare_significant_genes$

Figure 14: compare_significant_genes plot

Only a small subset of genes are differentially expressed in both LNCaP and PC3 (green labels), mostly along the x = y diagonal (e.g., CA9, IGFBP5, STC1, PPFIA4), indicating a shared hypoxia response. Genes significant in only one line cluster on the axes, suggesting cell line–specific effects.

8 🔬 Functional Enrichment Analysis

8.1 🎯 Pathway Analysis Strategy

To understand the biological significance of the differential expression patterns, we’ll perform a comprehensive functional enrichment analysis using multiple approaches:

Multi-level Analysis Approach:

Gene Set Enrichment Analysis (GSEA)
- Analyze entire ranked gene lists
- Capture subtle but coordinated changes
- Compare cell-line specific patterns
Over-representation Analysis (ORA)
- Focus on significantly changed genes
- Identify enriched pathways and processes
- Compare shared vs unique pathways
Network Analysis
- Visualize pathway interactions
- Identify key regulatory hubs
- Map cell-line specific networks

8.2 📊 Gene Set Enrichment Analysis

GSEA provides a powerful way to identify coordinated gene expression changes at the pathway level(Mootha et al. 2003). We’ll analyze both cell lines to understand shared and unique pathway responses to hypoxia.

We need to download the pathway files from the GSEA MSigDB webpage at: https://www.gsea-msigdb.org/gsea/msigdb/collections.jsp and load them in using fgsea::gmtPathways(). I will be working with the HALLMARK pathway set.

library(fgsea)
# read in file containing lists of genes for each pathway
hallmark_pathway <- gmtPathways("reference_genomes/h.all.v2024.1.Hs.symbols.gmt.txt")
head(names(hallmark_pathway))

#> [1] "HALLMARK_ADIPOGENESIS"        "HALLMARK_ALLOGRAFT_REJECTION" "HALLMARK_ANDROGEN_RESPONSE"  
#> [4] "HALLMARK_ANGIOGENESIS"        "HALLMARK_APICAL_JUNCTION"     "HALLMARK_APICAL_SURFACE"

🔍 The pathway file is loaded as a list of gene sets. We can access the genes for each pathway by selecting the pathway name using $:

head(hallmark_pathway$HALLMARK_HYPOXIA, 20)

#>  [1] "ACKR3"    "ADM"      "ADORA2B"  "AK4"      "AKAP12"   "ALDOA"    "ALDOB"    "ALDOC"    "AMPD3"    "ANGPTL4" 
#> [11] "ANKZF1"   "ANXA2"    "ATF3"     "ATP7A"    "B3GALT6"  "B4GALNT2" "BCAN"     "BCL2"     "BGN"      "BHLHE40"

After loading the pathways, we have to turn our ranked list into a vector, in which the log2FoldChange value is named with the gene name. We also need to get rid of any NA values or duplicate gene entries. The custom function prepare_ranked_list() will perform these operations.

# load the ranked list
lncap_ranked_list <- read.table("results/LNCAP_Hypoxia_vs_LNCAP_Normoxia_rank_2.rnk", header = TRUE, stringsAsFactors = F)
head(lncap_ranked_list)

# formats the ranked list for the fgsea() function
prepare_ranked_list <- function(ranked_list) { 
  # if duplicate gene names present, average the values
  if( sum(duplicated(ranked_list$Gene.name)) > 0) {
    ranked_list <- aggregate(.~Gene.name, FUN = mean, data = ranked_list)
    ranked_list <- ranked_list[order(ranked_list$log2FoldChange, decreasing = TRUE),]
  }
  # omit rows with NA values
  ranked_list <- na.omit(ranked_list)
  # turn the dataframe into a named vector
  ranked_list <- tibble::deframe(ranked_list)
  ranked_list
}

lncap_ranked_list <- prepare_ranked_list(lncap_ranked_list)
head(lncap_ranked_list)

#> SMIM10L3   PCDH19     NXF2     RYR3  CYP4F22 TNFRSF6B 
#>    10.93    10.66     8.74     8.51     7.80     7.69

Now that we have the named vector, we can plug it into the fgsea() function along with the hallmark pathways object. This will generate a table of results containing the enrichment scores associated with each pathway.

# generate GSEA results table using fgsea() by inputting the pathway list and ranked list
fgsea_results <- fgsea(pathways = hallmark_pathway,
                  stats = lncap_ranked_list,
                  minSize = 15,
                  maxSize = 500,
                  nperm= 1000)

fgsea_results %>% arrange (desc(NES)) %>% dplyr::select (pathway, padj, NES) %>% head()

The normalized enrichment scores (NES) tell us how much more enriched the pathway is in the hypoxia samples compared to the normoxia samples. As expected, we observe that the most enriched pathway in response to hypoxia is the HALLMARK_HYPOXIA pathway.

We can visualize the statistics for each pathway using a “waterfall” plot, which is a sideways bar plot of normalized enrichment scores for each of the pathways, color-coded by significance. This plot is great for quickly identifying the significantly enriched pathways.

waterfall_plot <- function (fsgea_results, graph_title) {
  fgsea_results %>% 
    mutate(short_name = str_split_fixed(pathway, "_",2)[,2])%>% # removes 'HALLMARK_' from the pathway title 
    ggplot( aes(reorder(short_name,NES), NES)) +
      geom_bar(stat= "identity", aes(fill = padj<0.05))+
      coord_flip()+
      labs(x = "Hallmark Pathway", y = "Normalized Enrichment Score", title = graph_title)+
      theme(axis.text.y = element_text(size = 7), 
            plot.title = element_text(hjust = 1))
}

p15 <- waterfall_plot(fgsea_results, "Hallmark pathways altered by hypoxia in LNCaP cells")
p15

Figure 15: Waterfall plot

🔬 Besides hypoxia, androgen response and MTORC1 signaling are also enriched under hypoxic conditions. Glycolysis is upregulated, while oxidative phosphorylation is downregulated, suggesting a metabolic shift similar to the Warburg effect. This may indicate hypoxia drives the switch from oxidative phosphorylation to glycolysis in tumors.

Interferon response pathways are negatively enriched in hypoxia, implying reduced interferon responsiveness, which is unexpected given the in vitro setting.

We can highlight specific pathways by plotting enrichment curves with fgsea::plotEnrichment(). Black ticks mark pathway genes; the green curve shows enrichment for hypoxia (left) or normoxia (right). Example curves are shown below.

# wrapper for fgsea::plotEnrichment()
plot_enrichment <- function (geneset, pathway, ranked_list) {
  plotEnrichment(geneset[[pathway]], ranked_list)+labs (title = pathway)
}
# example of positively enriched pathway (up in Hypoxia)
p16 <- plot_enrichment(hallmark_pathway, "HALLMARK_GLYCOLYSIS" , lncap_ranked_list)
p16

Figure 16: Enrichment plot

# example of negatively enriched pathway (down in Hypoxia)
p17 <- plot_enrichment(hallmark_pathway, "HALLMARK_OXIDATIVE_PHOSPHORYLATION" , lncap_ranked_list)
p17

Figure 17: Enrichment plot

8.3 📊 Downstream analysis with R

Gene Set Enrichment Analysis (GSEA) is a sophisticated computational method that evaluates whether pre-defined groups of genes (such as those associated with specific GO terms or KEGG pathways) exhibit statistically significant, coordinated expression changes between different biological conditions. Unlike traditional single-gene analyses, GSEA considers the entire ranked list of genes to identify biological pathways and processes that are systematically altered in the experimental condition.

This section demonstrates the implementation of GSEA using the clusterProfiler package(gencore.bio.nyu.edu 2021), a versatile R package that provides comprehensive tools for statistical analysis and visualization of functional profiles for genes and gene clusters. For detailed documentation and advanced usage, please refer to: https://bioconductor.org/packages/release/bioc/vignettes/clusterProfiler/inst/doc/clusterProfiler.html

8.3.1 📥 Prepare Input

# reading in data from deseq2
df = read.csv("results/res_2.csv")
# df = read.csv("data/drosphila_example_de.csv", header=TRUE)
# we want the log2 fold change 
original_gene_list <- df$log2FoldChange
# name the vector
names(original_gene_list) <- df$ensembl_id
# omit any NA values 
gene_list<-na.omit(original_gene_list)

# sort the list in decreasing order (required for clusterProfiler)
gene_list = sort(gene_list, decreasing = TRUE)
head(gene_list)

#> ENSG00000207005 ENSG00000286075 ENSG00000165194 ENSG00000269405 ENSG00000100101 ENSG00000198838 
#>           23.44           10.93           10.66            8.74            8.67            8.51

8.3.2 🔍 Gene Set Enrichment

Parameters: - keyType: Source of gene IDs (e.g., “ENTREZID”, “ENSEMBL”, “SYMBOL”). Check available options with keytypes(org.Dm.eg.db). - ont: Ontology; one of “BP”, “MF”, “CC”, or “ALL”. - nPerm: Number of permutations (higher = more accurate, slower). - minGSSize/maxGSSize: Minimum/maximum gene set size to include. - pvalueCutoff: P-value threshold. - pAdjustMethod: Adjustment method (e.g., “BH”, “bonferroni”).

# library(org.Dm.eg.db)
gse <- clusterProfiler::gseGO(geneList=gene_list, 
             ont ="ALL", 
             keyType = "ENSEMBL", 
             nPerm = 10000, 
             minGSSize = 3, 
             maxGSSize = 800, 
             pvalueCutoff = 0.05, 
             verbose = TRUE, 
             OrgDb = org.Hs.eg.db, 
             pAdjustMethod = "none")

8.4 Dotplot

require(DOSE)
p18 <- enrichplot::dotplot(gse, showCategory=10, split=".sign") + facet_grid(.~.sign) + scale_size(range = c(1, 8)) # Change the dot size range
p18

Figure 18: Dotplot

8.4.1 🗺️ Enrichment Map:

Enrichment map organizes enriched terms into a network with edges connecting overlapping gene sets. In this way, mutually overlapping gene sets are tend to cluster together, making it easy to identify functional modules.

### apply 'pairwise_termsim' before making plots
## calculate the similarity matrix for all gene sets, and not the top 200!
## note that this takes some time
gse_2 <- pairwise_termsim(gse, showCategory = dim(gse)[1] )

## Extract the gene sets. These are the 20 sets that are in the dotplot (10 activated, 10 supressed).
geneSets2plot <- p17$data$Description

## make a emapplot showing only the selected gene sets

p19 <- enrichplot::emapplot(gse_2, showCategory=geneSets2plot)
p19

$gse_emapplot$

Figure 19: Enrichment map

8.4.2 🌳 Tree plot

The treeplot() function performs hierarchical clustering of enriched terms. It relies on the pairwise similarities of the enriched terms calculated by the pairwise_termsim() function, which by default using Jaccard’s similarity index (JC). Users can also use semantic similarity values if it is supported (e.g., GO, DO and MeSH).

p20 <- treeplot(gse_2)
p20

$gse_treeplot$

Figure 20: Tree plot

8.4.3 🕸️ Category Netplot

The cnetplot depicts the linkages of genes and biological concepts (e.g. GO terms or KEGG pathways) as a network (helpful to see which genes are involved in enriched pathways and genes that may belong to multiple annotation categories).

# categorySize can be either 'pvalue' or 'geneNum'
p21 <- cnetplot(gse, categorySize="pvalue", foldChange=gene_list, showCategory = 3)
p21

Figure 21: Category netplot

8.4.4 📊 UpSet Plot

The upsetplot is an alternative to cnetplot for visualizing the complex association between genes and gene sets. It emphasizes the gene overlapping among different gene sets.

p22 <- upsetplot(gse)
p22

Figure 22: UpSet plot

8.4.5 ⛰️ Ridgeplot

Grouped by gene set, density plots are generated by using the frequency of fold change values per gene within each set. Helpful to interpret up/down-regulated pathways.

p23 <- ridgeplot(gse) + labs(x = "enrichment distribution")
p23

$gse_ridgeplot$

Figure 23: Ridge plot

8.4.6 📊 GSEA Plot

Shows the running enrichment score for a gene set (green line), with the maximum score (red line) and gene set members (black lines) along the ranked gene list. The ranked metric (log2 fold change) reflects gene–phenotype correlation.

Parameter: - Gene Set Integer: Index of the gene set in the gse object (1 = first gene set).

# Use the `Gene Set` param for the index in the title, and as the value for geneSetId
p24 <- gseaplot(gse, by = "all", title = gse$Description[1], geneSetID = 1)
p24

Figure 24: GSEA plot

8.4.7 📚 PubMed trend of enriched terms

Plots the number/proportion of publications trend based on the query result from PubMed Central.

terms <- gse$Description[1:3]
p25 <- pmcplot(terms, 2015:2024, proportion=FALSE)
p25

Figure 25: PubMed trend plot

9 🧮 KEGG Gene Set Enrichment Analysis

To run KEGG pathway enrichment with gseKEGG(), gene IDs must be converted using the bitr function from clusterProfiler. Some messages or warnings are normal.

Set fromType in bitr to match the annotation source (same as keyType in gseGO). Set toType to one of: ‘kegg’, ‘ncbi-geneid’, ‘ncib-proteinid’, or ‘uniprot’. For org.Dm.eg.db, use ‘ENTREZID’ (equivalent to ‘ncbi-geneid’).

Use original_gene_list as the input for conversion.

9.1 🔢 Prepare Input

# Convert gene IDs for gseKEGG function
# We will lose some genes here because not all IDs will be converted
ids<-clusterProfiler::bitr(names(original_gene_list), fromType = "ENSEMBL", toType = "ENTREZID", OrgDb=org.Hs.eg.db)
 # remove duplicate IDS (here I use "ENSEMBL", but it should be whatever was selected as keyType)
dedup_ids = ids[!duplicated(ids$ENSEMBL),]

# Create a new dataframe df2 which has only the genes which were successfully mapped using the bitr function above
df2 = df[df$ensembl_id %in% dedup_ids$ENSEMBL,]

# Create a new column in df2 with the corresponding ENTREZ IDs
df2$Y = dedup_ids$ENTREZID

# Create a vector of the gene unuiverse
kegg_gene_list <- df2$log2FoldChange

# Name vector with ENTREZ ids
names(kegg_gene_list) <- df2$Y

# omit any NA values 
kegg_gene_list<-na.omit(kegg_gene_list)

# *******************************************
kegg_gene_list <- kegg_gene_list[!duplicated(names(kegg_gene_list))]

# sort the list in decreasing order (required for clusterProfiler)
kegg_gene_list = sort(kegg_gene_list, decreasing = TRUE)

9.2 Create gseKEGG object

organism: 3-letter KEGG code (see: https://www.genome.jp/kegg/catalog/org_list.html). Define as kegg_organism for reuse.
nPerm: Number of permutations (higher = more accurate, slower).
minGSSize/maxGSSize: Minimum/maximum gene set size to include.
pvalueCutoff: P-value threshold.
pAdjustMethod: Adjustment method (e.g., “BH”, “bonferroni”).
keyType: One of ‘kegg’, ‘ncbi-geneid’, ‘ncib-proteinid’, or ‘uniprot’.

kegg_organism = "hsa"
kk2 <- gseKEGG(geneList     = kegg_gene_list,
               organism     = kegg_organism,
               nPerm        = 10000,
               minGSSize    = 3,
               maxGSSize    = 800,
               pvalueCutoff = 0.05,
               pAdjustMethod = "none",
               keyType       = "ncbi-geneid")

9.3 Dotplot

p26 <- dotplot(kk2, showCategory = 10, title = "Enriched Pathways" , split=".sign") + facet_grid(.~.sign) + scale_size(range = c(1, 8)) # Change the dot size range
p26

Figure 26: Dotplot

9.4 Encrichment map:

### apply 'pairwise_termsim' before making plots
## calculate the similarity matrix for all gene sets, and not the top 200!
## note that this takes some time
kk3 <- pairwise_termsim(kk2, showCategory = dim(kk2)[1] )

## Extract the gene sets. These are the 20 sets that are in the dotplot (10 activated, 10 supressed).
geneSets2plot_2 <- p23$data$Description

## make a emapplot showing only the selected gene sets

p27 <- enrichplot::emapplot(kk3, showCategory=geneSets2plot_2)
p27

$KEGG_emapplot$

Figure 27: Enrichment map

9.5 Category Netplot

The cnetplot shows connections between genes and biological categories (e.g., GO terms, KEGG pathways) as a network, highlighting shared genes across categories.

# categorySize can be either 'pvalue' or 'geneNum'
p28 <- cnetplot(kk2, categorySize="pvalue", foldChange=gene_list)
p28

$KEGG_cnetplot$

Figure 28: Category netplot

9.6 Ridgeplot

Helpful to interpret up/down-regulated pathways.

p29 <- ridgeplot(kk2) + labs(x = "enrichment distribution")
p29

$KEGG_ridgeplot$

Figure 29: Ridge plot

9.7 GSEA Plot

Standard visualization of GSEA results.

Parameter: - Gene Set Integer: Index of the gene set in the gse object (1 = first gene set; default is 1).

# Use the `Gene Set` param for the index in the title, and as the value for geneSetId
p30 <- gseaplot(kk2, by = "all", title = kk2$Description[1], geneSetID = 1)
p30

Figure 30: GSEA plot

9.8 KEGG Pathway Visualization with Pathview(Luo and Brouwer 2013)

Pathview visualizes gene expression on KEGG pathways, outputting PNG (native layout) and PDF (publication-ready) formats.

Key Parameters: - gene.data: Named vector of gene expression (e.g., kegg_gene_list), names = ENTREZ IDs, values = log2 fold changes - pathway.id: KEGG pathway ID (e.g., “hsa04130”), found in gseKEGG results; can be a vector - species: 3-letter KEGG organism code (e.g., “hsa”), must match gseKEGG analysis

library(pathview)

# Produce the native KEGG plot (PNG)
dme1 <- pathview(gene.data=kegg_gene_list, pathway.id="hsa04130", species = "hsa")

# Produce a different plot (PDF) (not displayed here)
dme2 <- pathview(gene.data=kegg_gene_list, pathway.id="hsa03060", species = "hsa", kegg.native = F)

knitr::include_graphics("hsa04130.pathview.png")

Figure 31: KEGG pathway diagram for hsa04130

10 🛞 Visualizing Genome-wide Expression Changes with Circos Plots

Circos plots are powerful tools for visualizing genomic data in a circular layout, allowing us to represent complex genomic relationships and patterns effectively. In this section, we’ll create a comprehensive Circos visualization that displays:

Chromosome-wise distribution of differentially expressed genes
Log2 fold changes mapped to their genomic locations
Significant genes highlighted with their corresponding positions
Potential genomic hotspots of transcriptional regulation

The visualization will help us identify any potential chromosomal regions that are particularly responsive to hypoxic conditions and understand the genome-wide distribution of our differentially expressed genes.

10.1 Preparing Data for Circos Plot(Gu et al. 2014)

# Load required libraries
library(biomaRt)

#' Function to get human gene coordinates from Ensembl
get_human_ensembl_coordinates <- function() {
    # Connect to Ensembl
    message("Connecting to Ensembl...")
    ensembl <- useEnsembl(biomart = "genes")
    
    # Find and set human dataset
    datasets <- listDatasets(ensembl)
    human_datasets <- grep("human", datasets$description, ignore.case = TRUE)
    
    if (length(human_datasets) == 0) {
        stop("No human dataset found in Ensembl")
    }
    
    dataset <- datasets$dataset[human_datasets[1]]  # Use first human dataset
    message(paste("Using dataset:", dataset))
    
    # Set dataset and get gene coordinates
    ensembl <- useDataset(dataset = dataset, mart = ensembl)
    
    # Define attributes for gene coordinates
    attributes <- c("ensembl_gene_id", "chromosome_name", 
                   "start_position", "end_position")
    
    # Get coordinates for all genes
    message("Retrieving gene coordinates...")
    all.genes <- getBM(attributes = attributes, 
                      values = list(ensembl_gene_id = c()), 
                      mart = ensembl)
    
    # Rename column to match DESeq2 output
    colnames(all.genes)[1] <- "ensembl_id"
    
    return(all.genes)
}

# Get gene coordinates
gene_coords <- get_human_ensembl_coordinates()

# Merge coordinates with DESeq2 results
message("Merging coordinates with DESeq2 results...")
merged_data <- merge(gene_coords, res_2, by = "ensembl_id", all.x = TRUE)

# Format chromosome names
merged_data$chromosome_name <- paste0("chr", merged_data$chromosome_name)

# Save complete dataset
output_dir <- "data"
dir.create(output_dir, showWarnings = FALSE, recursive = TRUE)

write.csv(merged_data, 
          file.path(output_dir, "deseq.csv"), 
          row.names = FALSE)

# Create and save subset of interesting genes
if (exists("genes_to_check")) {
    merged_data_subset <- merged_data[merged_data$Gene.Name %in% genes_to_check, ]
    write.csv(merged_data_subset, 
              file.path(output_dir, "deseq_subset.csv"), 
              row.names = FALSE)
    message(paste("Saved subset of", nrow(merged_data_subset), "genes"))
}

10.2 Creating the Circos Plot

# Load required packages
library(circlize)
library(dplyr)

# Prepare data for Circos plot
circos_data <- merged_data %>%
  # Filter out non-standard chromosomes
  filter(grepl("^chr[0-9XY]+$", chromosome_name)) %>%
  # Sort by chromosome and position
  arrange(chromosome_name, start_position) %>%
  mutate(log2fc = ifelse(is.na(log2FoldChange), 0, log2FoldChange))

# Initialize Circos plot with chromosomes
circos.clear()
message("Initializing Circos plot...")

# Set up Circos plot parameters
circos.par(
    "track.height" = 0.1, 
    "start.degree" = 90,
    gap.degree = 2
)

# Create base chromosome ideogram
circos.initializeWithIdeogram(
    species = "hg19",
    chromosome.index = paste0("chr", c(1:22, "X", "Y"))
)

# Add track for log2 fold changes
circos.genomicTrack(
    circos_data,
    panel.fun = function(region, value, ...) {
        circos.genomicPoints(
            region,
            value$log2fc,
            pch = 16,
            cex = 0.5,
            col = ifelse(value$log2fc > 0, "red", "blue")
        )
    },
    track.height = 0.2
)

# Add track for significant genes
message("Adding significant genes track...")
sig_genes <- circos_data %>% filter(padj < 0.05)
if (nrow(sig_genes) > 0) {
  # Prepare data for labels, ensuring correct column order
  sig_genes_for_labels <- sig_genes %>%
    dplyr::select(chromosome_name, start_position, end_position, Gene.name, log2fc)
  
  circos.genomicLabels(
    sig_genes_for_labels,
    labels.column = 4, # Gene.name column
    side = "inside",
    col = ifelse(sig_genes_for_labels$log2fc > 0, "red", "blue")
  )
}

# Save plot
pdf("figures/circos_plot.pdf", width = 10, height = 10)
dev.off()

Figure 32: Circos plot

11 💻 Comprehensive Analysis Conclusions

11.1 Summary of Key Findings

1. Cell Line-Specific Responses - LNCaP cells show strong androgen-dependent responses - PC3 cells exhibit enhanced EMT and invasiveness signatures - Distinct metabolic adaptations in each cell line

2. Shared Hypoxia Response - Common upregulation of glycolysis pathways - Coordinated angiogenic response - HIF-1α target gene activation

3. Metabolic Reprogramming - Shift from oxidative phosphorylation to glycolysis - Cell-specific lipid metabolism adaptations - Differential nutrient utilization strategies

11.2 Biological Implications

1. Cancer Progression - Hypoxia drives metabolic adaptation - Enhanced survival mechanisms - Potential metastatic capabilities

2. Therapeutic Relevance - Cell-type specific vulnerabilities - Common therapeutic targets - Resistance mechanisms

3. Clinical Applications - Biomarker identification - Treatment stratification - Drug response prediction

11.3 Future Directions

1. Additional Investigations - Validation of key findings - Protein-level confirmation - Metabolomic profiling

2. Therapeutic Development - Target validation studies - Drug combination strategies - Resistance mechanism studies

3. Clinical Translation - Biomarker development - Patient stratification - Treatment optimization

12 ℹ️ Session Info

sessionInfo()

#> R version 4.5.0 (2025-04-11 ucrt)
#> Platform: x86_64-w64-mingw32/x64
#> Running under: Windows 11 x64 (build 26100)
#> 
#> Matrix products: default
#>   LAPACK version 3.12.1
#> 
#> locale:
#> [1] LC_COLLATE=English_United States.utf8  LC_CTYPE=English_United States.utf8    LC_MONETARY=English_United States.utf8
#> [4] LC_NUMERIC=C                           LC_TIME=English_United States.utf8    
#> 
#> time zone: America/New_York
#> tzcode source: internal
#> 
#> attached base packages:
#> [1] grid      stats4    stats     graphics  grDevices utils     datasets  methods   base     
#> 
#> other attached packages:
#>  [1] viridis_0.6.5               viridisLite_0.4.2           showtext_0.9-7              showtextdb_3.0             
#>  [5] sysfonts_0.8.9              pdftools_3.5.0              DOSE_4.2.0                  Rtsne_0.17                 
#>  [9] ggupset_0.4.1               EnhancedVolcano_1.26.0      cowplot_1.2.0               scales_1.4.0               
#> [13] janitor_2.2.1               here_1.0.1                  biomaRt_2.64.0              org.Hs.eg.db_3.21.0        
#> [17] AnnotationDbi_1.70.0        pathview_1.48.0             enrichplot_1.28.4           clusterProfiler_4.16.0     
#> [21] fgsea_1.34.2                gridExtra_2.3               RColorBrewer_1.1-3          ggrepel_0.9.6              
#> [25] ComplexHeatmap_2.24.1       pheatmap_1.0.13             Matrix_1.7-3                data.table_1.17.8          
#> [29] lubridate_1.9.4             forcats_1.0.0               stringr_1.5.1               dplyr_1.1.4                
#> [33] purrr_1.1.0                 readr_2.1.5                 tidyr_1.3.1                 tibble_3.3.0               
#> [37] ggplot2_3.5.2               tidyverse_2.0.0             edgeR_4.6.3                 limma_3.64.3               
#> [41] DESeq2_1.48.2               SummarizedExperiment_1.38.1 Biobase_2.68.0              MatrixGenerics_1.20.0      
#> [45] matrixStats_1.5.0           GenomicRanges_1.60.0        GenomeInfoDb_1.44.2         IRanges_2.42.0             
#> [49] S4Vectors_0.46.0            BiocGenerics_0.54.0         generics_0.1.4              htmltools_0.5.8.1          
#> [53] kableExtra_1.4.0            knitr_1.50                 
#> 
#> loaded via a namespace (and not attached):
#>   [1] splines_4.5.0           klippy_0.0.0.9500       bitops_1.0-9            ggplotify_0.1.2        
#>   [5] filelock_1.0.3          R.oo_1.27.1             graph_1.86.0            XML_3.99-0.19          
#>   [9] lifecycle_1.0.4         httr2_1.2.1             doParallel_1.0.17       rprojroot_2.1.1        
#>  [13] vroom_1.6.5             lattice_0.22-6          magrittr_2.0.3          sass_0.4.10            
#>  [17] rmarkdown_2.29          jquerylib_0.1.4         yaml_2.3.10             ggtangle_0.0.7         
#>  [21] askpass_1.2.1           DBI_1.2.3               abind_1.4-8             R.utils_2.13.0         
#>  [25] RCurl_1.98-1.17         yulab.utils_0.2.1       rappdirs_0.3.3          circlize_0.4.16        
#>  [29] GenomeInfoDbData_1.2.14 tidytree_0.4.6          svglite_2.2.1           codetools_0.2-20       
#>  [33] DelayedArray_0.34.1     xml2_1.4.0              tidyselect_1.2.1        shape_1.4.6.1          
#>  [37] aplot_0.2.8             UCSC.utils_1.4.0        farver_2.1.2            BiocFileCache_2.16.2   
#>  [41] jsonlite_2.0.0          GetoptLong_1.0.5        iterators_1.0.14        systemfonts_1.2.3      
#>  [45] foreach_1.5.2           tools_4.5.0             progress_1.2.3          treeio_1.32.0          
#>  [49] snow_0.4-4              Rcpp_1.1.0              glue_1.8.0              SparseArray_1.8.1      
#>  [53] xfun_0.53               qvalue_2.40.0           withr_3.0.2             fastmap_1.2.0          
#>  [57] digest_0.6.37           timechange_0.3.0        R6_2.6.1                gridGraphics_0.5-1     
#>  [61] qpdf_1.4.1              textshaping_1.0.3       colorspace_2.1-1        GO.db_3.21.0           
#>  [65] RSQLite_2.4.3           R.methodsS3_1.8.2       prettyunits_1.2.0       httr_1.4.7             
#>  [69] S4Arrays_1.8.1          pkgconfig_2.0.3         gtable_0.3.6            blob_1.2.4             
#>  [73] XVector_0.48.0          clue_0.3-66             png_0.1-8               snakecase_0.11.1       
#>  [77] ggfun_0.2.0             rstudioapi_0.17.1       tzdb_0.5.0              reshape2_1.4.4         
#>  [81] rjson_0.2.23            nlme_3.1-168            curl_7.0.0              cachem_1.1.0           
#>  [85] GlobalOptions_0.1.2     parallel_4.5.0          pillar_1.11.0           vctrs_0.6.5            
#>  [89] dbplyr_2.5.0            cluster_2.1.8.1         Rgraphviz_2.52.0        evaluate_1.0.5         
#>  [93] KEGGgraph_1.68.0        cli_3.6.5               locfit_1.5-9.12         compiler_4.5.0         
#>  [97] rlang_1.1.6             crayon_1.5.3            plyr_1.8.9              fs_1.6.6               
#> [101] stringi_1.8.7           BiocParallel_1.42.1     assertthat_0.2.1        Biostrings_2.76.0      
#> [105] lazyeval_0.2.2          GOSemSim_2.34.0         hms_1.1.3               patchwork_1.3.2        
#> [109] bit64_4.6.0-1           KEGGREST_1.48.1         statmod_1.5.0           igraph_2.1.4           
#> [113] memoise_2.0.1           bslib_0.9.0             ggtree_3.16.3           fastmatch_1.1-6        
#> [117] bit_4.6.0               ape_5.8-1               gson_0.1.0

📚 References

Blighe K, Lewis M, Rana S. 2025. “EnhancedVolcano: Publication-Ready Volcano Plots with Enhanced Colouring and Labeling.” Web Page. https://bioconductor.org/packages/EnhancedVolcano. .

Durinck, S., P. T. Spellman, E. Birney, and W. Huber. 2009. “Mapping Identifiers for the Integration of Genomic Datasets with the r/Bioconductor Package biomaRt.” Journal Article. Nat Protoc 4 (8): 1184–91. https://doi.org/10.1038/nprot.2009.97.

Ewels, P. A., A. Peltzer, S. Fillinger, H. Patel, J. Alneberg, A. Wilm, M. U. Garcia, P. Di Tommaso, and S. Nahnsen. 2020. “The Nf-Core Framework for Community-Curated Bioinformatics Pipelines.” Journal Article. Nat Biotechnol 38 (3): 276–78. https://doi.org/10.1038/s41587-020-0439-x.

gencore.bio.nyu.edu. 2021. “Gene Set Enrichment Analysis with ClusterProfiler.” Web Page. https://learn.gencore.bio.nyu.edu/rna-seq-analysis/gene-set-enrichment-analysis/.

Gu, Z., L. Gu, R. Eils, M. Schlesner, and B. Brors. 2014. “Circlize Implements and Enhances Circular Visualization in r.” Journal Article. Bioinformatics 30 (19): 2811–12. https://doi.org/10.1093/bioinformatics/btu393.

He, J., L. Lin, and J. Chen. 2022. “Practical Bioinformatics Pipelines for Single-Cell RNA-Seq Data Analysis.” Journal Article. Biophys Rep 8 (3): 158–69. https://doi.org/10.52601/bpr.2022.210041.

Love, M. I., W. Huber, and S. Anders. 2014. “Moderated Estimation of Fold Change and Dispersion for RNA-Seq Data with DESeq2.” Journal Article. Genome Biol 15 (12): 550. https://doi.org/10.1186/s13059-014-0550-8.

Lu, Erick. 2020. “Bulk RNA-Sequencing Pipeline and Differential Gene Expression Analysis.” Web Page. https://erilu.github.io/bulk-rnaseq-analysis/.

Luo, W., and C. Brouwer. 2013. “Pathview: An r/Bioconductor Package for Pathway-Based Data Integration and Visualization.” Journal Article. Bioinformatics 29 (14): 1830–31. https://doi.org/10.1093/bioinformatics/btt285.

Mootha, V. K., C. M. Lindgren, K. F. Eriksson, A. Subramanian, S. Sihag, J. Lehar, P. Puigserver, et al. 2003. “PGC-1alpha-Responsive Genes Involved in Oxidative Phosphorylation Are Coordinately Downregulated in Human Diabetes.” Journal Article. Nat Genet 34 (3): 267–73. https://doi.org/10.1038/ng1180.

🧬 Bulk RNA-sequencing Pipeline and Differential Gene Expression Analysis 📊

👨‍🔬 Hongchen Xiao

📅 September 08, 2025

1 📦 Required Packages

2 📊 Analysis Overview

2.1 ↗️ Upstream Analysis

2.2 ➡️ Midstream Analysis

2.3 ⬇️Downstream Analysis

3 🔬 Upstream Data Processing

3.1 📋 Project Overview

3.1.1 🔄 Pipeline Description

3.1.2 📊 Dataset Description

3.1.2.1 🧪 Experimental Design

3.1.3 🧬 Sample Information

3.2 📥 Data Download and Preparation

3.2.1 🛠️ Setting Up SRA Toolkit

3.2.2 📝 Sample List Preparation

3.2.3 💻 Computing Environment

3.2.4 📥 Download SRA Files

3.2.5 🔄 FASTQ File Processing

3.2.5.1 🧪 LNCaP Sample Processing

3.2.5.2 PC3 Sample Processing

3.2.5.3 🧹 Cleanup

3.3 🧬 Read Mapping with nf-core/rnaseq

3.3.1 🔄 Pipeline Overview

3.3.2 📋 Sample Sheet Preparation

3.3.2.1 Sample Sheet Format

3.3.3 🧬 Reference Genome Preparation

3.3.3.1 📥 Download Reference Files

3.3.4 🚀 Pipeline Execution

3.3.4.1 🏃 Execute the Pipeline

3.3.5 📊 Understanding the output

4 🎡 Differential Expression Analysis

4.1 📊 Introduction to DESeq2

4.2 📥 Data Import and Preprocessing

4.2.1 📊 Examining Sequencing Depth

4.2.2 ⚖️ Need for Normalization

4.3 ⚙️ DESeq2 Setup and Configuration

4.3.1 🔧 Creating the DESeq2 Object

4.3.2 📋 Preparing Sample Groups

4.3.3 📝 Creating Sample Metadata

4.3.4 Initializing the DESeq2 Object

4.3.5 🚀 Running the DESeq2 Analysis

4.3.6 🔍 Examining DESeq2 Results

4.3.6.1 📊 Raw Count Data

4.3.6.2 ⚖️ Normalized Count Data

5 🏷️ Adding Gene Annotations

5.1 🔍 Using BioMart for Gene Information

5.2 📥 Loading Annotations

5.3 🔄 Combining Expression and Annotation Data

6 🔍 Quality Control and Exploratory Analysis

6.1 📊 Sample Variability Assessment

6.1.1 🔄 Data Transformation

6.1.2 📊 Sample Distance Analysis

6.1.3 🔍 Analysis of Highly Variable Genes

6.1.4 📈 Principal Component Analysis

7 🧫 Cell Line-Specific Analysis

7.1 📋 Overview

7.2 📊 Analysis Strategy

7.3 🔬 Cell Line Data Preparation

7.4 🔬 Analyzing Cell-Specific Responses

7.4.1 📊 Visualizing Cell-Specific Expression Patterns

7.5 📊 Differential Expression Results

7.6 📊 Results Visualization

7.6.1 📈 PlotCounts

7.6.2 🌡️ Differential gene heatmap

7.6.3 🌋 Volcano Plot Analysis

7.6.4 🌋 Enhanced volcano plots(Blighe K 2025)

7.6.5 💾 Save processed results for further analysis

7.7 📈 LogFoldChange comparison plot

8 🔬 Functional Enrichment Analysis

8.1 🎯 Pathway Analysis Strategy

8.2 📊 Gene Set Enrichment Analysis

8.3 📊 Downstream analysis with R

8.3.1 📥 Prepare Input

8.3.2 🔍 Gene Set Enrichment

8.4 Dotplot

8.4.1 🗺️ Enrichment Map:

8.4.2 🌳 Tree plot

8.4.3 🕸️ Category Netplot